Investigation of laboratory saw ginning and blending on cotton fiber quality measurement

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The objective of this study was to examine the effects of two different laboratory saw ginning (SG) rates and subsequent mechanical blending on fiber high volume instrument (HVI) and advanced fiber information system (AFIS) quality measurement. Seed cottons from diverse Upland cotton cultivars, years, and locations were evaluated. As SG rate was increased, HVI results for uniformity index (UI), short fiber index (SFI), and two non-lint parameters (Area%, particle count) and also AFIS results for some properties were impacted, with significant effects on HVI UI and two AFIS short fiber content (SFC) indices. Apparent fiber blending impacts were observed for HVI UI, strength (STR), Rd, SFI, and two trashes as well as for all AFIS parameters, with significant effects on HVI Rd and two trashes and all AFIS parameters except the length and maturity measurements. A combination of ginning and blending process indicated statistically significant interactions for HVI STR, Rd, SFI, and two trashes, and also for AFIS neps, L(w) CV, UQL(w), L(n) CV, SFC(n), L5%(n), immature fiber content (IFC), fineness, total count, dust count, particle count, and visible foreign matter (VFM). Further analysis implied a few impacts of ginning and blending on correlations between HVI and relevant AFIS qualities. Despite the observation, laboratory saw gin and blending methods should continue to be a practical tool to study the differences when all samples are processed identically. AFIS blending cotton fiber HVI laboratory saw gin ginning rate Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Introduction Cotton is one of the most important agricultural commodities mainly for its naturally produced textile fiber (Robertson & Roberts 2010 ). Fiber quality is critical in cotton production and sustainability (i.e., reduced water usage and improved soil health and bioactivity) as well as in the growers and processors’ profitability (Bradow & Davidonis 2000 ). After seed cottons being harvested from the field by mechanical harvesters and ginned, the quality of lint is determined in the laboratory (Anthony 1994 ; Armijo et al. 2023 ; Blake et al. 2022 ; Boykin 2008 ; Byler & Delhom 2012 ; Hardin et al. 2018 ; Holt & Laird 2008 ; Whitelock et al. 2019 ). The qualities of U.S. cotton are classified officially with the Uster high volume instrument (HVI) measurements and the classer’s determination of extraneous matter levels (Cotton Incorporated 2018 ; Delhom et al. 2020 ). Another commercially available and accepted instrument known as advanced fiber information system (AFIS) is not used for cotton quality classification, instead is used for process control in textile mills and research purpose in laboratories (Delhom et al. 2018 ; Kelly et al. 2012 , 2015 ; Paudel et al. 2013 ). HVI reports the fiber micronaire (MIC), upper-half mean length (UHML), length uniformity index (UI), strength, color (color grade, Rd, +b), short fiber index (SFI), and non-lint content (particle count, percentage area (Area%), leaf grade) from testing a bundle of fibers, while AFIS provides 20 fiber quality parameters including fiber length and distribution, trash content, nep content, fineness and maturity from testing individualized (or single) fibers. At times, small scale research or individual seed cotton samples from breeders and geneticists are insufficient in quantity to be ginned at a commercial gin. Hence, laboratory ginning equipment (saw or roller gins) or hand ginning method is used to collect the fiber samples from different seed cotton sources, including manually picked single boll cottons and mechanically harvested commercial cottons (Armijo et al. 2023 ; Boykin 2008 ; Boykin et al. 2010 ; Hinze et al. 2023 , 2024 ; Rodgers et al. 2015 ). Extensive studies have been performed to investigate whether different types of laboratory ginning methods impact fiber HVI and AFIS quality measurement. For example, Boykin et al. ( 2010 ) compared several AFIS fiber properties between seed cottons processed with 4 laboratory gins and 2 commercial gin stands. Their results showed the differences in AFIS fiber length, short fiber content (SFC), fineness, immature fiber content (IFC), maturity ratio (MR), neps, and seed-coat neps between laboratory gins and commercial gins. They observed that differences between the laboratory gins and the commercial gins were not consistent from one gin facility to the next. They also found that the fiber properties of lint samples from the 10-saw New Dennis laboratory gin (Dennis Manufacturing, Athens, TX) were the most similar to those from commercial gin stand, indicating the use of laboratory gins as an effective and convenient screening tool for cotton researchers predicting fiber quality in commercial gins. Rodgers et al. ( 2015 ) examined the effects of three laboratory ginning methods (saw ginning, roller ginning, hand ginning) and blending on HVI, Fibronaire, and near infrared (NIR) MIC measurements of seed cotton bolls from three cotton varieties. They reported that laboratory ginning and blending influences on MIC property were less than 0.2 unit between MIC measurement methods (HVI vs. Fibronaire), inferring that the use of any gin methods would provide acceptable and comparable MIC results. Armijo et al. ( 2023 ) compared the HVI fiber quality of three diverse cultivars (2 Upland and 1 Pima) between laboratory and commercial scale saw and roller gin stands. They detailed some differences of HVI properties on Upland and Pima cultivars between laboratory gins and commercial gins, and further addressed the awareness of when using a laboratory roller gin to select Upland or Pima breeding lines that will be commercially saw ginned. To quantify and verify any HVI and AFIS quality differences of fiber samples from three different laboratory gins, Hinze et al. ( 2023 , 2024 ) processed boll samples from different cultivars grown in two environments and observed some fiber quality differences among three gins. Overall, researchers have been utilizing laboratory gins to acquire fiber quality characteristics by sticking with one gin and one setting to process their samples in one study. However, the setting (specifically ginning rate) can be quite different between similar type of laboratory gins, for example, a saw speed of 476 for New Dennis 10-saw laboratory gin and of 1355 for Custom Fabrication and Repairs (CFR) 10-saw laboratory gin (Boykin et al. 2010 ). Furthermore, there is scarce research studying whether different ginning rates of one laboratory gin affect fiber qualities. Although mechanical harvesting and subsequent ginning could bring in some blending effect in the lint, at times further mechanical blending was performed at the laboratories to ensure the uniformity, reliability, and validity of any fiber property results (Paudel et al. 2013 ; Robert et al. 2005 ; Rodgers et al. 2015 ). Blending aligns and orients the fiber in a common longitudinal direction and smooths the fiber surface, thereby changing the fiber packing / openness. However, there has been limited research on laboratory fiber blending effect on HVI and AFIS measurement and also the correlations between two measurements of diverse cotton cultivars, likely due to a labor-intensive and time-consuming blending process. The objective of this study was to determine how well fiber HVI and AFIS quality agree from a low laboratory saw ginning (SG) rate with those from a high SG rate, and also to comprehend how the blending impacts HVI and AFIS quality of samples at different laboratory SG rates. Table 1 summarizes the brief specifications between laboratory SG in this study and full-size SG stands reported previously (Blake et al. 2022 ; Hughs & Armijo 2015 ). As expected, laboratory SG setup has much lower saw speed than respective full-size SG stands (198 ~ 347 vs. 784 ~ 814 m/min), and also less ginning rate (0.08 vs. 28 ~ 40 kg/min) than full-size stands. Comparison of blended fibers with unblended fibers would help to reveal the degree to which variation in unblended cotton can be controlled by the ginning rate and sample blending approaches. Table 1 Laboratory SG and full-size SG specifications Saw-ginning (SG) Saw diameter, cm Saw speed, RPM Saw speed, m/min No. of saw Teeth per saw Ginning rate, kg/min Full-size stands 30 ~ 40 615 ~ 850 784 ~ 814 46 ~ 116 328 ~ 352 28 ~ 40 Laboratory 12.7 497 ~ 870 198 ~ 347 10 137 0.08 Materials and methods Seed cotton samples, ginning, and fiber quality measurement The seed cotton samples consisted of a total 25 commercial Upland samples that were harvested by mechanical harvesters. These 25 samples were from 5 crop years (2016, 2018, 2020, 2021, 2022), grown in 2 U.S. states (Mississippi, New Mexico), and from 22 commercial cotton cultivars of five brands (ALL-TEX/DYNA-GRO, Americot, BASF, Deltapine, and Phytogen). None of these 25 samples was from the same cultivar in the same year and the same location, hence data from 25 samples were analyzed. They were stored at a laboratory condition for at least 2 days before the ginning. After removing apparent non-lint materials (such as large sticks and burrs), 250.0 g of seed cotton was ginned respectively using a 10-saw Dennis laboratory saw gin at two SG speeds and feeding the sample within three minutes, approximately with a saw loading of 0.5 kg/saw/h that was much lower than reported loading of 49.4 or 52.8 kg/saw/h for commercial gin stands (Blake et al., 2022 ). Two SG speed settings were adjusted on a fixed driver pulley (Diameter = 2.0 in, RPM = 1740) and a fixed saw (Diameter = 5.0 in) with a driven pulley (Diameter = 7.0 in, saw RPM = 497, and saw tip speed = 198 m/min) for a low ginning rate (GR 1) and another driven pulley (Diameter = 4.0 in, saw RPM = 870, and saw tip speed = 347 m/min) for a high ginning rate (GR 2). The lint collected from each seed cotton sample was weighed on a balance to determine lint turnout by dividing the lint weight with the original weight of seed cotton. Half of the lint sample was bagged “as is” (unblended) for HVI and AFIS test, while another half was blended through a laboratory mechanical fiber blender (Model CS-45, Custom Scientific Inc.) following a reported procedure (Robert et al. 2005 ) prior to HVI and AFIS analysis. This blender consists of a rotatable cylinder (Diameter = 10.0 in, Width = 5 in) with stripper fillet clothing having 1-in wire spines with forwardly-inclined tips for collecting and holding fibers in layers, and the surface speed of the cylinder is 62 m/min approximately. Each seed cotton sample was processed twice (n = 2), and the treatments (ginning and blending sequence) were randomized within each ginning rate. The lint samples were conditioned at 21 ± 1°C temperature and 65 ± 2% relative humidity for 24 hours before HVI and AFIS testing. Mean HVI properties of each sample were determined by an HVI 1000 (USTER Technologies Inc., Knoxville, TN) with five replications per sample, and mean AFIS values were assessed by an AFIS Pro 2 (USTER Technologies Inc., Knoxville, TN) with three replications of 5000 fibers per measurement. Averages of HVI and AFIS data from n = 2 replicate samples were used. Data Analysis Regression analyses on any HVI or AFIS quality pair from 25 samples (average of 2 replicates per sample) between the treatments (SG rate and blending) were calculated using Microsoft® Excel® for Office 365. Statistical analyses were also performed on the data using analysis of variance (ANOVA) function under Data Analysis in Microsoft® Excel® for Office 365 with a 95% confidence level and using analysis of covariance (ANCOVA) available from http://www.biostathandbook.com/ancova.html (accessed July 29, 2024). Results and discussion Ginning rate and blending on fiber HVI quality The impact of ginning rate on HVI measurements (mean and standard deviation (SD)) of all unblended samples between GR 1 and GR 2 is summarized in Table 2 . Variations of these HVI values could represent their variabilities in commercial cotton bales and cotton breeding programs. There were no statistically significant differences ( p -value > 0.05) for these HVI properties but UI ( p -value ≤ 0.05). In other words, these HVI properties are without a significant sample by SG interaction. Since UI is the ratio of the average mean length (ML) of the fibers to the UHML and then is multiplied by 100, at least one of the ML and the UHML impacts the UI determination. Considering the facts that (i) UHML mean ± SD (1.19 ± 0.04) of the GR 1 samples was not different from that (1.19 ± 0.04) of the GR 2 samples (Table 2 ) and (ii) there were not any correlations (R 2 = 0.07) between the UI and UHML for both the GR 1 and GR 2 samples (Fig. 1 a), the ML of fibers would influence the UI value primarily (Fig. 1 b) . When plotting one HVI quality of unblended GR 1 samples against the same one of unblended GR 2 samples, the regression lines indicated the variations in the slope, adjusted intercept, and R 2 (Table 2 and Fig. 2 ). The slope value of less than 0.90, between 0.90 and 1.10, or greater than 1.10 indicates the less, little, or more change of y-axis variable than that of x-axis variable. Hence, UI value of the GR 2 samples in y-axis increased more than UI value of the GR 1 samples in x-axis due to the slope = 1.13, MIC, UHML, STR, Rd, +b, and SFI indices of the GR 2 samples changed synchronously with those of the GR 1 samples owing to the slope = 0.90 to 1.00, whereas two trash indices (Area%, particle count) of the GR 2 samples decreased more than those of the GR 1 samples due to the slope = 0.53 to 0.66. Meanwhile, absolute adjusted intercept (or ׀adjusted intercept׀) suggested a range of < 0.10 for MIC, UHML, STR, Rd, +b, and SFI, a range of between 0.10 and 0.30 for UI (-0.12), and a range of greater than 0.30 for two trash indices (0.37 to 0.39). In addition, MIC, UHML, STR, Rd, and + b showed a high R 2 (> 0.85), followed by the UI and SFI (R 2 = 0.68 to 0.77) and the two trashes (R 2 = 0.41 to 0.56). Because the slope, ׀adjusted intercept׀, and R 2 in Table 2 and Fig. 2 were from one seed cotton sample undergone two SG processing, ideally the slope and R 2 would be close to 1.0 while the ׀adjusted intercept׀ would be close to 0.0 when SG-dependent quality change was non-existent or minimal. Under this scenario, thresholds for the slope, ׀adjusted intercept׀, and R 2 would be considered to be 0.90 to 1.10, 0.85, respectively. This led to five HVI measurements (MIC, UHML, STR, Rd, +b) falling into these criteria. Hence, the ginning rate impacted fiber UI greatly and significantly, and also SFI and two trash components largely. Reasonably, some seed cotton samples would exhibit relatively apparent difference for any one of these HVI qualities between the two SG rates. As an example, Fig. 3 shows a significant and strong correlation between fiber MIC from the GR 1 samples and those from the GR 2 samples (slope = 0.97, ׀adjusted intercept׀ = 0.02, R 2 = 0.93). Four samples were observed to have a MIC absolute difference of > 0.20. Among these four samples, one (MIC 4.0) decreased in MIC at GR 2 compared to GR 1. These MIC differences were checked against AFIS fineness, IFC, and MR values, and it was found that the differences were related with corresponding IFC differences negatively and moderately (R 2 = 0.19), with fineness differences positively and moderately (R 2 = 0.15), and with MR differences positively and weakly (R 2 = 0.10). The effect of ginning rate on UI is clearly visible in Fig. 4 , with relatively a larger UI reading at GR 2 than at GR 1. Higher UI values, resulted from higher average lengths among the GR 2 samples, could be due to a number of shorter fibers being blown out the lint collection cage at higher GR or being brushed away from the beard of fibers in HVI test. Five samples were found to have a UI increase of > 1.0 with ginning rate increasing. Further analysis revealed unclear effect of cultivar, crop years, and grown locations on distinctive UI difference in Fig. 4 . The effect of blending on HVI measurements is compared in Table 3 . There were statistically significant differences for Rd and two trash indices between unblended and blended samples for each of two SG rates. During the blending process, non-lint materials were falling from the sample, causing the two trash reading reduction and the Rd value increasing apparently. For instance, particle count decreased from 81.14 to 40.16 for the GR 1 samples and from 73.10 to 45.14 for the GR 2 samples, whereas the Rd improved from 73.08 to 77.10 for the GR 1 samples and from 73.71 to 76.86 for the GR 2 samples. Although there was statistically significant difference for UI in Table 2 , there were insignificant differences for UI between unblended and blend samples in Table 3 . Independent of ginning rate, MIC, UHML, and + b values between unblended and blended samples showed a satisfactory slope of 0.88 to 1.04, an ׀adjusted intercept׀ of less than 0.10, and a R 2 of 0.91 to 0.97 (Table 3 and Fig. 5 ). Contrarily, Rd values indicated a moderate slope of 0.80 to 0.82, a poor ׀adjusted intercept׀ of around 0.23, and a R 2 of 0.80 to 0.92, whereas two trash indices exhibited both a low slope (0.44 to 0.59) and a low R 2 (0.39 to 0.65). In addition, UI, STR, and SFI from the GR 1 samples revealed an acceptable slope of 0.84 to 1.04, an ׀adjusted intercept׀ of 0.03 to 0.19, and an R 2 of 0.71 to 0.94, but these properties from the GR 2 samples became worse in the slope of 0.64 to 0.76, ׀adjusted intercept׀ of 0.20 to 0.30, and R 2 of 0.47 (except for UI with R 2 = 0.80 and STR with R 2 = 0.87). Therefore, blending procedure influenced fiber UI, STR, Rd, SFI, and two trash indices apparently. On the basis of two correlation lines between unblended and blended samples from two ginning rates, analysis of covariance (ANCOVA) in Table 3 highlighted the statistically significant differences in both slope and intercept for STR, insignificant differences in slope but significant differences in intercept for Rd, SFI, and two trash indices, and insignificant differences in both slope and intercept for MIC, UHML, UI, and + b. This observation implied statistically significant sample by ginning and by blending interactions for STR, Rd, SFI, Area%, and particle count, or statistically insignificant sample x ginning x blending interaction for the remaining HVI properties (MIC, UHML, UI, +b). Figures 6 and 7 show the relationships between unblended and blended samples from two ginning rates for MIC and UI, respectively. Two correlation lines in Fig. 6 resembled each other and went along with diagonal line satisfactorily, but it showed one GR 1 sample and one GR 2 sample with an MIC absolute difference of > 0.20 following sample blending. Although being similar to MIC in ANCOVA statistics ( p -value > 0.05 in both slope and intercept), UI property in Fig. 7 suggested a different pattern of UI responding to blending process, underscoring the effect of blending on UI measurement. Three GR 1 samples and one GR 2 sample were observed to have UI increase of > 1.0. Although the difference of UI for the GR 1 samples or for the GR 2 samples was insignificant following the blending, there were a mean UI increase for both GR 1 samples (83.05 to 83.41) and GR 2 samples (83.56 to 83.68). Higher UI values might imply a loss of shorter fibers being spun away during the blending or being brushed away from the beard of fibers in HVI test. Ginning rate and blending on fiber AFIS quality Table 4 details the impact of the ginning rates on AFIS qualities from unblended samples between GR 1 and GR 2. Two SFC properties (SFC(w) and SFC(n)) indicated significant differences between 2 ginning rates and suggested a significant sample by ginning interaction. As given in Fig. 8 , only UQL(w) and L5%(n) revealed a good slope of 0.91 to 0.95, a least ׀adjusted intercept׀ of < 0.10, and a moderate R 2 of 0.73 to 0.76 among 20 AFIS properties, Comparatively, there were five of nine HVI measurements (MIC, UHML, STR, Rd, +b) with a slope of 0.90 to 1.10, an ׀adjusted intercept׀ of 0.85. Therefore, the ginning rate influenced nearly all AFIS measurements greatly but was less for UQL(w) and L5%(n) relatively. A distinctive difference between the 2 measurements is that HVI tests a bundle of fibers, while AFIS measures the individualized fibers. Correlations of selected AFIS qualities (UQL(w), MR, and fineness) from unblended samples between GR 1 and GR 2 are shown in Figs. 9 , 10 , and 11 , respectively. Increase of ginning rate led to an UQL(w) increase of > 0.04 for two samples and an UQL(w) decrease of > 0.04 for two samples (Fig. 9 ). Also, ginning rate altered MR in Fig. 10 , with a MR increase of > 0.02 for four samples having MR values 0.02 for one sample having a MR value around 0.90. In other words, high GR might have a possibility of producing a greater MR value when a sample’s MR was < 0.86. Like MR, AFIS fineness tended to be larger for lower fineness samples ( 6.0 and 3 samples with fineness decrease of > 6.0, and these 7 samples were distributed equally along the 45-degree diagonal line. This observation suggested a relatively larger effect of GR on MR than on fineness. The reason might be due to the fact that AFIS MR is calculated from the percentages of both mature fiber content (Ɵ ≥ 0.50) and immature fiber content (Ɵ < 0.25) by measuring the fiber shape, whereas AFIS fineness is determined by the algorithm based on the shape and form of the fibers and also their specific weight. Statistical significances for nep size, neps, SCN size, SCN count, total count, trash size, dust count, particle count, and visible foreign matter (VFM) difference between unblended and blended samples were noted for least one of two SG rates (Table 5 ). In other words, blending process did not impact fiber AFIS length and maturity measurements significantly. SCN size showed very weak correlations (R 2 = 0.02 to 0.03) between unblended and blended samples for two ginning rates, and will not be discussed. Blending of either GR 1 or GR 2 samples resulted in greater neps for blended samples than for unblended samples, for example, a neps increasing from 239.94 to 401.74 for the GR 1 samples and from 213.82 to 307.70 for the GR 2 samples. That is, mechanical blending of the GR 1 samples (slope = 1.46) produced more neps than that of the GR 2 samples (slope = 1.08). To understand the blending effect, the neps differences by subtracting neps values of the blended samples from those of the unblended ones were linked with fineness, IFC, and MR values of unblended samples. The neps differences were related with MR negatively and greatly (R 2 = 0.44), followed with fineness negatively (R 2 = 0.14) and with IFC positively (R 2 = 0.10) for the GR 1 samples. In contrast, the neps differences were not associated with MR (R 2 < 0.08), fineness (R 2 = 0.10), and IFC (R 2 < 0.01) among the GR 2 samples. Hence, blending caused more neps in low MR samples ginned at low ginning rate. All AFIS properties but neps from the GR 1 samples revealed a slope of 0.38 to 0.87, an ׀adjusted intercept׀ of 0.01 to 0.63, and a R 2 of 0.24 to 0.88, whereas those from the GR 2 samples suggested a slope range of 0.29 to 1.12, an ׀adjusted intercept׀ of 0.01 to 0.72, and a R 2 range of 0.08 to 0.94 (Fig. 12 ). Clearly, blending of the GR 2 samples improved the measurement agreement for length and SFC indices, fineness, and MR with a fitting slope of 0.94 to 1.12 for the GR 2 samples compared to that of 0.55 to 0.87 for the GR 1 samples, accompanied by a reduced ׀adjusted intercept׀ of 0.01 to 0.13 for the GR 2 samples against that of 0.13 to 0.48 for the GR 1 samples, and by an enhanced R 2 of 0.73 to 0.94 for the GR 2 samples relative to that of 0.52 to 0.88 for the GR 1 samples. Therefore, the blending process influenced all AFIS properties completely. ANCOVA analysis revealed statistically significant differences in slope but insignificant differences in intercept for L(w) CV, UQL(w), L(n) CV, SFC(n), and fineness, and also insignificant differences in slope but significant differences in intercept for neps, IFC, total count, dust count, particle count, and VFM. This result implied statistically significant sample x ginning x blending interactions for neps, L(w) CV, UQL(w), L(n) CV, SFC(n), IFC, fineness, total count, dust count, particle count, and VFM. Other AFIS properties (nep size, SCN count, L(w), SFC(w), L(n), L5%(n), MR, and trash size) might not be affected by sample x ginning x blending interaction. Figures 13 through 15 depict the correlations of selected AFIS qualities (UQL(w), MR, and fineness) between unblended samples and blended samples from GR 1 and GR 2 test, respectively. Compared to the GR 1 samples, the GR 2 samples indicated a good agreement for UQL(w) between unblended and blended samples with a desired slope, least intercept, and elevated R 2 (Fig. 13 ). Blending process of the GR 1 samples caused an UQL(w) increase of > 0.04 for four samples and a UQL(w) decrease of > 0.04 for three samples, while the process of the GR 2 samples did not induce an UQL(w) difference of > 0.04 for any sample. The UQL(w) differences were correlated positively and greatly with fineness (R 2 = 0.34) and MR (R 2 = 0.25) but negatively and weakly with IFC (R 2 = 0.20) for the GR 1 samples, whereas the differences were not connected with fineness (R 2 < 0.05), MR (R 2 < 0.01), and IFC (R 2 < 0.05) for the GR 2 samples. Therefore, blending raised UQL(w) values for high fineness samples ginned at low ginning rate. Similarly, blending improved MR and fineness regression statistics more for the GR 2 samples than for the GR 1 samples (Figs. 14 and 15 ). Six GR 1 samples were noted to have a MR increase of > 0.02 and six GR 2 samples were to have a MR increase of > 0.02, implying the potential of causing greater MR values for some samples when they were blended (Fig. 14 ). MR alike, fineness increases of > 6.0 were observed among ten GR 1 samples and nine GR 2 samples, but fineness decreases of > 6.0 were noted on two GR 1 samples (Fig. 15 ). This result suggested increased fineness readings of some samples if they were blended. Ginning rate and blending on the correlations of fiber HVI against AFIS quality Table 6 outlines the effects of both GR and blending on the correlations between HVI and relevant AFIS qualities from unblended samples to those blended. Regardless of GR setting, HVI UHML showed the greatest correlations with UQL(w) and L5%(n) (R 2 = 0.83 to 0.94), followed with L(w) (R 2 = 0.41 to 0.70), and the least with L(n) (R 2 = 0.02 to 0.13) for the unblended samples. This pattern remained unchanged among the blended samples, except for the two pairs of UHML vs. UQL(w) and UHML vs. L5%(n) from the GR 1 samples with a lower R 2 (= 0.64). Comparison of HVI UHML to AFIS lengths is of great interest, since HVI UHML determines the fiber length from the fibrogram of scanning a fiber beard that many short fibers may not protrude far enough to be scanned, while AFIS lengths characterize the complete within-sample distribution of all single fibers by weight and by number. Similar to HVI UHML that reflects the average length by number of the longer half of the fibers by weight, AFIS UQL(w) characterizes the fiber lengths of the longer 25% of all fibers by weights and L5%(n) measures the fiber lengths of the longer 5% of all fiber by number. Therefore, a high R 2 between HVI UHML and AFIS UQL(w) or L5%(n) than between HVI UHML and AFIS L(w) or L(n) is expected. Overall, HVI UHML had a greater correlation with AFIS L(w) (R 2 = 0.31 to 0.70) than with AFIS L(n) (R 2 = 0.02 to 0.13) without the consideration of ginning and blending effects. The tendency was similar to a reported Pearson correlation coefficient ( R ) of 0.923 between HVI UHML and AFIS L(w) as well as of 0.823 between HVI UHML and AFIS L(n) (R 2 = 0.02 to 0.13) for samples with HVI UHML from 0.96 to 1.20 inch (van der Sluijs et al., 2021 ). ANCOVA result of unblended samples indicated statistically significant differences in intercept for the four pairs (UHML vs. L(w), UHML vs. UQL(w), UHML vs. L(n), and UHML vs. L5%(n)), while those of blended samples implied insignificant differences statistically in both slope and intercept for any pairs. As an example, Fig. 16 depicts two correlation lines of HVI UHML vs. AFIS UQL(w) from the GR 1 and GR 2 samples between unblended and blended process, with a notable worsening R 2 from 0.83 to 0.64 for the GR 1 samples following the blending process. Independent of both GR and blending, HVI SFI was observed to have moderate correlations with SFC(w) (R 2 = 0.12 to 0.29) but weak correlations with SFC(n) (R 2 = 0.07 to 0.19), in which the pattern resembles a report between HVI SFI against SFC(w) ( R = 0.777) and against SFC (n) ( R = 0.655) (van der Sluijs et al., 2021 ). Meanwhile, a decreasing R 2 between HVI SFI and SFC(w) or SFC(n) echoes a reduced R 2 from HVI UHML vs. AFIS L(w) (R 2 = 0.31 to 0.70) to HVI UHML vs. AFIS L(n) (R 2 = 0.02 to 0.13). ANCOVA result showed statistically insignificant differences in slope and in intercept for four pairs excepting one pair (SFI vs. SFC(w)) of unblended samples, which indicated a significant difference in intercept. Relatively, HVI MIC showed great correlations with fineness (R 2 = 0.61 to 0.92), followed with IFC (R 2 = 0.56 to 0.72) and MR (R 2 = 0.53 to 0.71). In general, GR or blending did not impact the relationships of any pairs clearly, except that blending process reduced the MIC vs. fineness correlation of GR 1 samples from R 2 = 0.92 to 0.61, which was accompanied by a statistically significant difference in slop (ANCOVA p -value = 0.01). Opposite to L(w) vs. L(n) relationship possessing a moderate R 2 of 0.62 to 0.72, two pairs (UQL(w) vs. L5%(n) and SFC(w) vs. SFC(n)) showed a strong R 2 of > 0.95 to 0.98. Both GR and blending did not affect the correlations of these pairs obviously, as the data were presented by the weight or the number of the fibers in a sample from one measurement. In general, a few effects of both GR and blending on the correlations between HVI and relevant AFIS qualities were noticed. For instance, three pairs (UHML vs. UQL(w), UHML vs. L5%(n), and MIC vs. fineness) of lower GR samples showed a reduced R 2 from unblended to blended samples. Four pairs (UHML vs. L(w), UHML vs. UQL(w), UHML vs. L(n), and UHML vs. L5%(n)) suggested statistically significant differences in intercept among unblended samples, while one pair (MIC vs. fineness) indicated statistically significant difference in slope among blended samples. Especially, a R 2 > 0.90 might hint the consistency of measuring fiber UHML or UQL(w) or L5%(n) quality on higher GR samples from HVI or AFIS independent of fiber blending. Ginning rate and lint turnout The lint turnout for the GR 1 samples was in good agreement with that for the GR 2 samples (a slope of 1.00, an adjusted intercept of 0.05, and a R 2 of 0.83), and there was no statistically significant difference ( p -value = 0.47) between the two (Fig. 17 ). Although the average lint turnout (= 41.9%) of the GR 1 samples was similar to that (= 41.6%) of the GR 2 samples, eight samples from the GR 2 samples had a lint turnout deduction of > 1.0% compared to the GR 1 samples, whereas one of four Deltapine samples from the GR 2 samples had a lint turnout increase of > 1.0%. The lint turnout (41.6% ~ 41.9%) from mechanically harvested seed cottons in this study was a little lower than those of 41.9% ~ 43.4% from 30 clean bolls each of three seed cotton Upland varieties (Rodgers et al. 2015 ). Conclusion This study compared fiber HVI and AFIS quality of diversified lint ginned with a laboratory 10-saw gin at two GRs (lower GR 1 vs. higher GR 2) and then blended with a laboratory mechanical blender. With GR increasing, HVI results for UI, SFI, and two trashes and also AFIS results for nearly all properties were impacted, with statistically significant effects on HVI UI and on 2 AFIS SFC parameters. For example, higher GR process enhanced the HVI UI property significantly, however it did not improve UHML in the same way. Also, GR elevation might generate a greater MR or fineness value for samples with lower MR or fineness, and also could impact MR property more than fineness property. The effect of fiber blending on HVI UI, STR, Rd, SFI, and two trashes as well as on all AFIS results was observed, with significant effects on HVI Rd and two trashes as well as without significant impact on AFIS length and maturity measurements. Although blending did not cause the difference of HVI UI significantly for any of 2 GRs, there was a little UI increase following the blending of samples. Blending caused neps increasing more for low GR samples than for high GR samples, whereas the process improved MR or fineness agreement more for high GR samples than for low GR samples. When HVI and AFIS data was correlated to determine if relevant properties from HVI and AFIS measurements responded similarly to ginning and blending process, it was found that three pairs (UHML vs. UQL(w), UHML vs. L5%(n), and MIC vs. fineness) of lower GR samples showed worsening correlations from unblended to blended samples. Relative to one pair (MIC vs. fineness) of blended samples revealing statistically significant difference in slope, other four pairs (UHML vs. L(w), UHML vs. UQL(w), UHML vs. L(n), and UHML vs. L5%(n)) of unblended samples indicated statistically significant differences in intercept. Overall effect of both ginning and blending process suggested statistically significant interactions for HVI STR, Rd, SFI, and two trashes, and also for AFIS neps, L(w) CV, UQL(w), L(n) CV, SFC(n), L5%(n), IFC, fineness, total count, dust count, particle count, and VFM. Despite the observation, laboratory saw gin or other ginning methods with or without blending should continue to be useful tools for exploring sample differences from fiber HVI and/or AFIS properties as long as an identical procedure is applied to all samples. Declarations Acknowledgements Authors wish to acknowledge two ARS Cotton Ginning laboratories (New Mexico and Mississippi) in providing seed cotton samples, Ms M Dunn in cotton ginning, and Ms H King in fiber HVI and AFIS measurement. We also thank Mr D McAlister of ARS SRRC Senior Textile Advisor and Dr C Armijo of ARS Cotton Ginning Research for providing expertise insights for improving the quality of the manuscript. Mention of a product or specific equipment does not constitute a guarantee or warranty by the U.S. Department of Agriculture and does not imply its approval to the exclusion of other products that may also be suitable. Authors' contributions Delhom C conceived the research. Liu Y collected cotton samples and conducted data analysis. Both authors prepared the manuscript and approved the final manuscript. Funding This research was supported by the USDA-ARS Research Project # 6054-44000-080-00D. Availability of data and material All data used or generated are included in the manuscript. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests Authors do not have competing interests. Authors’ details 1 USDA, ARS, Southern Regional Research Center (SRRC), Cotton Quality and Innovation Research Unit, New Orleans, LA 70124, USA 2 USDA, ARS, Sustainable Water Management Research Unit, Stoneville, MS 38776, USA References Anthony, WS. The effect of gin machinery on measurement of high volume instrument color and trash of cotton. Trans ASAE. 1994; 37:373-380. Armijo, CB, Tumuluru, JS, Delhom CD, Whitelock DP, Funk PA, Wanjura JD, Kothari N, and Martin VB. Investigating fiber quality from tabletop and commercial scale saw and roller gin stands. Proceeding of 2023 Beltwide Cotton Conferences. 2023; pp. 490-491. Blake, CD, Whitelock DP, Funk PA, Armijo CB, and Buser MD. The impact of ginning rate on fiber and seed quality. Appl Eng Agric. 2022; 38:9-14. Boykin, JC. Small sample techniques to evaluate cotton variety trials. J Cotton Sci. 2008; 12:16–32. Boykin JC, Whitelock DP, Armijo CB, Buser MD, Holt GA, Valco TD, Findley DS, Barnes EM, and Watson MD. Predicting fiber quality after commercial ginning based on fiber obtained with laboratory-scale gin stands. J Cotton Sci. 2010; 14:34–45. Bradow J, Davidonis G. Quantitation of fiber quality and the cotton production-processing interface: a physiologist's perspective. J Cotton Sci. 2000; 4:34-64. Byler RK, Delhom CD. Comparison of saw ginning and high-speed roller ginning with different lint cleaners of mid-south grown cotton. Appl Eng Agric. 2012; 28:475-482. Cotton Incorporated. The classification of cotton. Cotton Incorporated, Cary, NC. 2018. Delhom CD, Kelly B, Martin V. Physical properties of cotton fiber and their measurement. In: Fang DD, editors. Cotton Fiber: Physics, Chemistry and Biology. Cham, Switzerland: Springer; 2018. p 41-73. Delhom CD, Knowlton J, Martin VB, Blake C. The classification of Cotton. J Cotton Sci. 2020; 24:189–196. Hardin IV RG, Barnes EM, Valco TD, Martin VB, Culp DM. Effects of gin machinery on cotton quality. J Cotton Sci. 2018; 22:36-46. Hinze L, Scheffler J, Thompson A, Hague S, Udall J, Kothari N. Comparison of fiber quality parameters from laboratory ginning systems used in cotton breeding programs. Proceeding of 2023 Beltwide Cotton Conferences. 2023; p 527. Hinze L, Scheffler J, Thompson A, Hague S, Udall J, Kothari N. Comparison of fiber quality parameters from laboratory ginning systems used in cotton breeding programs. Proceeding of 2024 Beltwide Cotton Conferences. 2024; p 431. Hughs SE, Armijo CB. Impact of gin saw-tooth design on fiber and textile processing quality. J Cotton Sci. 2015; 19:27-32. Holt GA, Laird JW. Initial fiber quality comparisons of the power roll gin stand to three different makes of conventional gin stands. Appl Eng Agric.2008;24: 295-299. Kelly C, Hequet E, Dever J. Interpretation of AFIS and HVI fiber property measurements in breeding for cotton fiber quality improvement. J Cotton Sci. 2012; 16:1-16. Kelly B, Abidi N, Ethridge D, Hequet EF. Fiber to fabric. In Fang DD, Percy RG, editors. Cotton, 2nd edition. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, 2015. p 665-744. Paudel D, Hequet E, Abidi N. Evaluation of cotton fiber maturity measurements. Ind Crop Prod. 2013; 45:435–441. Robert KQ, Dunn MC, Cosenza FA. Blending of cotton fiber samples for length measurement. Proceeding of 2005 Beltwide Cotton Conferences. 2005. p 2718-2730. Robertson WC, Roberts BA. Integrated crop management for cotton production in the 21 st century. In Wakelyn PJ, Chaudhry MR, editors. Cotton: Technology for the 21st Century. Washington DC, USA: International Cotton Advisory Committee. 2010. p 63-97. Rodgers J, Fortier C, Delhom C, Cui X. Laboratory ginning and blending impacts on cotton fiber micronaire measurements. AATCC J Res. 2015; 2:1-7. van der Sluijs MHJ, Delhom CD, Martin VB. Assessment of cotton fibre length measurement methods. J Text I. 2021; 112:1377–1389. Whitelock DP, Buser MD, Armijo CB, Hughs SE. The impact of historical gin stand technologies on cotton fiber and seed quality. Appl Eng Agric. 2019; 35:775-785. Tables Tables 2-6 are available in the Supplementary Files section. Supplementary Files Tables26.docx Cite Share Download PDF Status: Published Journal Publication published 02 Dec, 2025 Read the published version in Journal of Cotton Research → Version 1 posted Editorial decision: Major revision 04 Jun, 2025 Reviewers agreed at journal 17 Mar, 2025 Reviewers invited by journal 17 Mar, 2025 Editor assigned by journal 14 Mar, 2025 First submitted to journal 13 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6222116","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":429932766,"identity":"8ac974a8-b060-4de3-940d-77fb3dbc41eb","order_by":0,"name":"Yongliang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIie3QMUvDQBTA8XccZGoz16l+hCeBogjGj/KOgi4igktBwRMhLrobKPYr1MX55IFZDrtmjLh0cDh3B9MmdEua0eH+w93jwQ+OA/D5/msFrk6pDUzK23QhtCaiJLYzgYqASDoQzB55SRdH58Njccv9Z47DzMjlT9JC7MfJPuH4cs8Izf1XVqml4CBtI/nZCAmletEVITQURD27ldzUZMoxLoqtJCoIWc1gRTSLeU7yqzdpJjvWjspPztRcCv02fT9Vaf6ZiKcWEmYPkXO/V2p2f8fu+/owDhdjdg6bya6BYLB+IW92Ihk0A4ChBunqYVO18fl8Pl/dH7BKXq4UZIytAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0785-5621","institution":"USDA-ARS Cotton Structure and Quality Research Unit","correspondingAuthor":true,"prefix":"","firstName":"Yongliang","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-03-13 17:46:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6222116/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6222116/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s42397-025-00238-w","type":"published","date":"2025-12-02T15:58:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78809077,"identity":"17937b39-587c-4bb9-b1af-e6ce02273c8a","added_by":"auto","created_at":"2025-03-19 08:43:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93129,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of (a) HVI UHML vs. HVI UI correlations and (b) HVI UHML vs. HVI ML correlations between the GR 1 and GR 2 samples (unblended).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/05b7c90a2a9d017a77de6279.png"},{"id":78810790,"identity":"5c1797f6-52a2-4be3-a43d-be768cfc868d","added_by":"auto","created_at":"2025-03-19 08:59:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":148832,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the slope, absolute adjusted intercept (or ׀adjusted intercept׀), and R\u003csup\u003e2\u003c/sup\u003e of HVI regressions between unblended GR 1 and GR 2 samples, sorted by the ׀adjusted intercept׀ decreasing. Three rectangles showed the desired ranges for the slope (0.90 ~ 1.10), R\u003csup\u003e2 \u003c/sup\u003e(\u0026gt; 0.85),\u003csup\u003e \u003c/sup\u003eand ׀adjusted intercept׀ (\u0026lt; 0.10), respectively.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/28341d294bedf2b595351745.png"},{"id":78809585,"identity":"3e0c2bf4-2ca4-433c-8bf1-64daa537da2c","added_by":"auto","created_at":"2025-03-19 08:51:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85647,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of MIC measurement for unblended samples between GR 1 (relatively low SG rate) and GR 2 (relatively high SG rate). Encircled 4 samples showed an MIC absolute difference of \u0026gt; 0.20.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/33bf90a01d8ec54dbf648545.png"},{"id":78809082,"identity":"11e50553-4f5b-477c-9ccb-45186983054a","added_by":"auto","created_at":"2025-03-19 08:43:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59266,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of UI measurement for unblended samples between GR 1 and GR 2. Encircled 5 samples showed a UI absolute difference of \u0026gt; 1.0.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/50b5ddd3632878ef3e07f263.png"},{"id":78809083,"identity":"b69ff23d-393c-44a1-ba18-3b1bc9897cc2","added_by":"auto","created_at":"2025-03-19 08:43:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":167544,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the slope, R\u003csup\u003e2\u003c/sup\u003e, and ׀adjusted intercept׀ of HVI regression lines between unblended and blended GR 1 or GR 2 samples, sorted by the slope and R\u003csup\u003e2\u003c/sup\u003e increasing while ׀adjusted intercept׀ decreasing for GR 1 samples, respectively. Three rectangles showed the desired ranges for the slope (0.90 ~ 1.10), R\u003csup\u003e2 \u003c/sup\u003e(\u0026gt; 0.85),\u003csup\u003e \u003c/sup\u003eand ׀adjusted intercept׀ (\u0026lt; 0.10), respectively.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/18949e9d9f34288b780feefb.png"},{"id":78809091,"identity":"7377219e-4483-450a-a1e5-b107570c035d","added_by":"auto","created_at":"2025-03-19 08:43:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":93779,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of MIC measurement between unblended samples and blended samples from GR 1 and GR 2. Encircled 2 samples showed an MIC absolute difference of \u0026gt; 0.20.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/cbaa48e6006c44404563d0ba.png"},{"id":78809590,"identity":"4632d7f9-b28b-4356-81b5-d655620211a6","added_by":"auto","created_at":"2025-03-19 08:51:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":112535,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of UI measurement between unblended samples and blended samples from GR 1 and GR 2. Encircled 3 samples showed a UI absolute difference of \u0026gt; 1.0.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/e6356f8b6f8ff614401fe030.png"},{"id":78809089,"identity":"11ff7441-6926-4611-8829-6b0d2287253b","added_by":"auto","created_at":"2025-03-19 08:43:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":208512,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the slope, absolute adjusted intercept (׀adjusted intercept׀) and R\u003csup\u003e2\u003c/sup\u003e of AFIS regression lines between unblended GR 1 and GR 2 samples, sorted by the (׀adjusted intercept׀) decreasing. Three rectangles showed the desired ranges for the slope (0.90 ~ 1.10), R\u003csup\u003e2 \u003c/sup\u003e(\u0026gt; 0.85),\u003csup\u003e \u003c/sup\u003eand ׀adjusted intercept׀ (\u0026lt; 0.10), respectively.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/39df52528a892f38ad2a5189.png"},{"id":78809085,"identity":"4d05eb9a-f3aa-42da-b99a-2ae8e706e5ea","added_by":"auto","created_at":"2025-03-19 08:43:43","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":96442,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of UQL(w) measurement for unblended samples between GR 1and GR 2. Encircled 4 samples showed a UQL(w) absolute difference of \u0026gt; 0.04.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/a8411504f70ab43bf3a53c3c.png"},{"id":78809601,"identity":"89ff3323-e4d5-4312-ad96-b4e558204ac9","added_by":"auto","created_at":"2025-03-19 08:51:43","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":143652,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of MR measurement for unblended samples between GR 1 and GR 2. Encircled 5 samples showed a MR absolute difference of \u0026gt; 0.02.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/ea1b114aa81c4ab414c7a09f.png"},{"id":78809593,"identity":"42e37d2f-edff-4c94-9c0a-1de47658908e","added_by":"auto","created_at":"2025-03-19 08:51:43","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":139387,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of fineness measurement for unblended samples between GR 1 and GR 2. Encircled 7 samples showed a fineness absolute difference of \u0026gt; 6.0.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/d02f1a73c4145d5d049ecb40.png"},{"id":78810792,"identity":"97b483e8-b647-43d4-b3ef-58715e2f544c","added_by":"auto","created_at":"2025-03-19 08:59:43","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":226380,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the slope, R\u003csup\u003e2\u003c/sup\u003e, and absolute adjusted intercept (׀adjusted intercept׀) of AFIS regression lines between unblended and blended GR 1 or GR 2 samples, sorted by the slope and R\u003csup\u003e2\u003c/sup\u003e increasing while ׀adjusted intercept׀) decreasing for GR 1 samples, respectively. Three rectangles showed the desired ranges for the slope (0.90 ~ 1.10), R\u003csup\u003e2 \u003c/sup\u003e(\u0026gt; 0.85),\u003csup\u003e \u003c/sup\u003eand ׀adjusted intercept׀ (\u0026lt; 0.10), respectively.\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/8b6a82bd83dd9be579d812a9.png"},{"id":78810988,"identity":"4cc10953-a3dc-4538-a726-625d63282b1f","added_by":"auto","created_at":"2025-03-19 09:07:43","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":170189,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of UQL(w) measurement between unblended samples and blended samples from GR 1 and GR 2. Encircled 6 samples showed a UQL(w) absolute difference of \u0026gt; 0.04.\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/085f293f45c62206cfdfa84b.png"},{"id":78809104,"identity":"8d979f66-cf2d-4600-b107-b81e93a10b9d","added_by":"auto","created_at":"2025-03-19 08:43:43","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":174910,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of MR measurement between unblended samples and blended samples from GR 1 and GR 2. Encircled 12 samples showed a MR absolute difference of \u0026gt; 0.02.\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/44828f0a1d57b050b83033eb.png"},{"id":78809113,"identity":"b0ab8c4f-ddf6-4987-8fcd-160a6077307a","added_by":"auto","created_at":"2025-03-19 08:43:44","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":178652,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of fineness measurement between unblended samples and blended samples from GR 1 and GR 2. Encircled 21 samples showed a fineness absolute difference of \u0026gt; 6.0.\u003c/p\u003e","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/7d7f0cb2f3ab4cac613977ee.png"},{"id":78809102,"identity":"053f59b1-e6d6-4ca4-b199-c54d623fb2d1","added_by":"auto","created_at":"2025-03-19 08:43:43","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":124418,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of HVI UHML vs. AFIS UQL(w) correlations between unblended samples and blended samples from GR 1 and GR 2.\u003c/p\u003e","description":"","filename":"floatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/83209a4b690ba884264295a5.png"},{"id":78809099,"identity":"b0dde591-7c56-4a50-b508-1a2b0f591520","added_by":"auto","created_at":"2025-03-19 08:43:43","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":135461,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of lint turnout between GR 1 samples and GR 2 samples. Encircled 9 samples showed a lint turnout absolute difference of \u0026gt; 1.0%.\u003c/p\u003e","description":"","filename":"floatimage17.png","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/c95aceb23f63efbc2717d99a.png"},{"id":97723980,"identity":"c7b1aa54-6275-4b20-bbad-2cdcbc18107a","added_by":"auto","created_at":"2025-12-08 16:10:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3079929,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/990adf3d-5963-4380-b13d-d46113031b46.pdf"},{"id":78809587,"identity":"420529b8-c071-4656-a1a3-9865c7c15d74","added_by":"auto","created_at":"2025-03-19 08:51:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":38593,"visible":true,"origin":"","legend":"","description":"","filename":"Tables26.docx","url":"https://assets-eu.researchsquare.com/files/rs-6222116/v1/ecddc38762997313a76d1b8b.docx"}],"financialInterests":"","formattedTitle":"Investigation of laboratory saw ginning and blending on cotton fiber quality measurement","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCotton is one of the most important agricultural commodities mainly for its naturally produced textile fiber (Robertson \u0026amp; Roberts \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Fiber quality is critical in cotton production and sustainability (i.e., reduced water usage and improved soil health and bioactivity) as well as in the growers and processors\u0026rsquo; profitability (Bradow \u0026amp; Davidonis \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). After seed cottons being harvested from the field by mechanical harvesters and ginned, the quality of lint is determined in the laboratory (Anthony \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Armijo et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Blake et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Boykin \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Byler \u0026amp; Delhom \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hardin et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Holt \u0026amp; Laird \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Whitelock et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The qualities of U.S. cotton are classified officially with the Uster high volume instrument (HVI) measurements and the classer\u0026rsquo;s determination of extraneous matter levels (Cotton Incorporated \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Delhom et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Another commercially available and accepted instrument known as advanced fiber information system (AFIS) is not used for cotton quality classification, instead is used for process control in textile mills and research purpose in laboratories (Delhom et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kelly et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Paudel et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). HVI reports the fiber micronaire (MIC), upper-half mean length (UHML), length uniformity index (UI), strength, color (color grade, Rd, +b), short fiber index (SFI), and non-lint content (particle count, percentage area (Area%), leaf grade) from testing a bundle of fibers, while AFIS provides 20 fiber quality parameters including fiber length and distribution, trash content, nep content, fineness and maturity from testing individualized (or single) fibers.\u003c/p\u003e \u003cp\u003eAt times, small scale research or individual seed cotton samples from breeders and geneticists are insufficient in quantity to be ginned at a commercial gin. Hence, laboratory ginning equipment (saw or roller gins) or hand ginning method is used to collect the fiber samples from different seed cotton sources, including manually picked single boll cottons and mechanically harvested commercial cottons (Armijo et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Boykin \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Boykin et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hinze et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rodgers et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Extensive studies have been performed to investigate whether different types of laboratory ginning methods impact fiber HVI and AFIS quality measurement. For example, Boykin et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) compared several AFIS fiber properties between seed cottons processed with 4 laboratory gins and 2 commercial gin stands. Their results showed the differences in AFIS fiber length, short fiber content (SFC), fineness, immature fiber content (IFC), maturity ratio (MR), neps, and seed-coat neps between laboratory gins and commercial gins. They observed that differences between the laboratory gins and the commercial gins were not consistent from one gin facility to the next. They also found that the fiber properties of lint samples from the 10-saw New Dennis laboratory gin (Dennis Manufacturing, Athens, TX) were the most similar to those from commercial gin stand, indicating the use of laboratory gins as an effective and convenient screening tool for cotton researchers predicting fiber quality in commercial gins. Rodgers et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) examined the effects of three laboratory ginning methods (saw ginning, roller ginning, hand ginning) and blending on HVI, Fibronaire, and near infrared (NIR) MIC measurements of seed cotton bolls from three cotton varieties. They reported that laboratory ginning and blending influences on MIC property were less than 0.2 unit between MIC measurement methods (HVI vs. Fibronaire), inferring that the use of any gin methods would provide acceptable and comparable MIC results. Armijo et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) compared the HVI fiber quality of three diverse cultivars (2 Upland and 1 Pima) between laboratory and commercial scale saw and roller gin stands. They detailed some differences of HVI properties on Upland and Pima cultivars between laboratory gins and commercial gins, and further addressed the awareness of when using a laboratory roller gin to select Upland or Pima breeding lines that will be commercially saw ginned. To quantify and verify any HVI and AFIS quality differences of fiber samples from three different laboratory gins, Hinze et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) processed boll samples from different cultivars grown in two environments and observed some fiber quality differences among three gins. Overall, researchers have been utilizing laboratory gins to acquire fiber quality characteristics by sticking with one gin and one setting to process their samples in one study. However, the setting (specifically ginning rate) can be quite different between similar type of laboratory gins, for example, a saw speed of 476 for New Dennis 10-saw laboratory gin and of 1355 for Custom Fabrication and Repairs (CFR) 10-saw laboratory gin (Boykin et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Furthermore, there is scarce research studying whether different ginning rates of one laboratory gin affect fiber qualities.\u003c/p\u003e \u003cp\u003eAlthough mechanical harvesting and subsequent ginning could bring in some blending effect in the lint, at times further mechanical blending was performed at the laboratories to ensure the uniformity, reliability, and validity of any fiber property results (Paudel et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Robert et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Rodgers et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Blending aligns and orients the fiber in a common longitudinal direction and smooths the fiber surface, thereby changing the fiber packing / openness. However, there has been limited research on laboratory fiber blending effect on HVI and AFIS measurement and also the correlations between two measurements of diverse cotton cultivars, likely due to a labor-intensive and time-consuming blending process. The objective of this study was to determine how well fiber HVI and AFIS quality agree from a low laboratory saw ginning (SG) rate with those from a high SG rate, and also to comprehend how the blending impacts HVI and AFIS quality of samples at different laboratory SG rates. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the brief specifications between laboratory SG in this study and full-size SG stands reported previously (Blake et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hughs \u0026amp; Armijo \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As expected, laboratory SG setup has much lower saw speed than respective full-size SG stands (198\u0026thinsp;~\u0026thinsp;347 vs. 784\u0026thinsp;~\u0026thinsp;814 m/min), and also less ginning rate (0.08 vs. 28\u0026thinsp;~\u0026thinsp;40 kg/min) than full-size stands. Comparison of blended fibers with unblended fibers would help to reveal the degree to which variation in unblended cotton can be controlled by the ginning rate and sample blending approaches.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLaboratory SG and full-size SG specifications\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaw-ginning (SG)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaw diameter, cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSaw speed, RPM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSaw speed, m/min\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo. of saw\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTeeth per saw\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGinning rate, kg/min\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull-size stands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026thinsp;~\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e615\u0026thinsp;~\u0026thinsp;850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e784\u0026thinsp;~\u0026thinsp;814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46\u0026thinsp;~\u0026thinsp;116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e328\u0026thinsp;~\u0026thinsp;352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28\u0026thinsp;~\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e497\u0026thinsp;~\u0026thinsp;870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e198\u0026thinsp;~\u0026thinsp;347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSeed cotton samples, ginning, and fiber quality measurement\u003c/h2\u003e \u003cp\u003eThe seed cotton samples consisted of a total 25 commercial Upland samples that were harvested by mechanical harvesters. These 25 samples were from 5 crop years (2016, 2018, 2020, 2021, 2022), grown in 2 U.S. states (Mississippi, New Mexico), and from 22 commercial cotton cultivars of five brands (ALL-TEX/DYNA-GRO, Americot, BASF, Deltapine, and Phytogen). None of these 25 samples was from the same cultivar in the same year and the same location, hence data from 25 samples were analyzed. They were stored at a laboratory condition for at least 2 days before the ginning.\u003c/p\u003e \u003cp\u003eAfter removing apparent non-lint materials (such as large sticks and burrs), 250.0 g of seed cotton was ginned respectively using a 10-saw Dennis laboratory saw gin at two SG speeds and feeding the sample within three minutes, approximately with a saw loading of 0.5 kg/saw/h that was much lower than reported loading of 49.4 or 52.8 kg/saw/h for commercial gin stands (Blake et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Two SG speed settings were adjusted on a fixed driver pulley (Diameter\u0026thinsp;=\u0026thinsp;2.0 in, RPM\u0026thinsp;=\u0026thinsp;1740) and a fixed saw (Diameter\u0026thinsp;=\u0026thinsp;5.0 in) with a driven pulley (Diameter\u0026thinsp;=\u0026thinsp;7.0 in, saw RPM\u0026thinsp;=\u0026thinsp;497, and saw tip speed\u0026thinsp;=\u0026thinsp;198 m/min) for a low ginning rate (GR 1) and another driven pulley (Diameter\u0026thinsp;=\u0026thinsp;4.0 in, saw RPM\u0026thinsp;=\u0026thinsp;870, and saw tip speed\u0026thinsp;=\u0026thinsp;347 m/min) for a high ginning rate (GR 2). The lint collected from each seed cotton sample was weighed on a balance to determine lint turnout by dividing the lint weight with the original weight of seed cotton. Half of the lint sample was bagged \u0026ldquo;as is\u0026rdquo; (unblended) for HVI and AFIS test, while another half was blended through a laboratory mechanical fiber blender (Model CS-45, Custom Scientific Inc.) following a reported procedure (Robert et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) prior to HVI and AFIS analysis. This blender consists of a rotatable cylinder (Diameter\u0026thinsp;=\u0026thinsp;10.0 in, Width\u0026thinsp;=\u0026thinsp;5 in) with stripper fillet clothing having 1-in wire spines with forwardly-inclined tips for collecting and holding fibers in layers, and the surface speed of the cylinder is 62 m/min approximately. Each seed cotton sample was processed twice (n\u0026thinsp;=\u0026thinsp;2), and the treatments (ginning and blending sequence) were randomized within each ginning rate.\u003c/p\u003e \u003cp\u003eThe lint samples were conditioned at 21\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C temperature and 65\u0026thinsp;\u0026plusmn;\u0026thinsp;2% relative humidity for 24 hours before HVI and AFIS testing. Mean HVI properties of each sample were determined by an HVI 1000 (USTER Technologies Inc., Knoxville, TN) with five replications per sample, and mean AFIS values were assessed by an AFIS Pro 2 (USTER Technologies Inc., Knoxville, TN) with three replications of 5000 fibers per measurement. Averages of HVI and AFIS data from n\u0026thinsp;=\u0026thinsp;2 replicate samples were used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eRegression analyses on any HVI or AFIS quality pair from 25 samples (average of 2 replicates per sample) between the treatments (SG rate and blending) were calculated using Microsoft\u0026reg; Excel\u0026reg; for Office 365. Statistical analyses were also performed on the data using analysis of variance (ANOVA) function under Data Analysis in Microsoft\u0026reg; Excel\u0026reg; for Office 365 with a 95% confidence level and using analysis of covariance (ANCOVA) available from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.biostathandbook.com/ancova.html\u003c/span\u003e\u003cspan address=\"http://www.biostathandbook.com/ancova.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed July 29, 2024).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003ch2\u003eGinning rate and blending on fiber HVI quality\u003c/h2\u003e\n\u003cp\u003eThe impact of ginning rate on HVI measurements (mean and standard deviation (SD)) of all unblended samples between GR 1 and GR 2 is summarized in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Variations of these HVI values could represent their variabilities in commercial cotton bales and cotton breeding programs. There were no statistically significant differences (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05) for these HVI properties but UI (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.05). In other words, these HVI properties are without a significant sample by SG interaction. Since UI is the ratio of the average mean length (ML) of the fibers to the UHML and then is multiplied by 100, at least one of the ML and the UHML impacts the UI determination. Considering the facts that (i) UHML mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04) of the GR 1 samples was not different from that (1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04) of the GR 2 samples (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) and (ii) there were not any correlations (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.07) between the UI and UHML for both the GR 1 and GR 2 samples (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea), the ML of fibers would influence the UI value primarily (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb) .\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen plotting one HVI quality of unblended GR 1 samples against the same one of unblended GR 2 samples, the regression lines indicated the variations in the slope, adjusted intercept, and R\u003csup\u003e2\u003c/sup\u003e (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The slope value of less than 0.90, between 0.90 and 1.10, or greater than 1.10 indicates the less, little, or more change of y-axis variable than that of x-axis variable. Hence, UI value of the GR 2 samples in y-axis increased more than UI value of the GR 1 samples in x-axis due to the slope\u0026thinsp;=\u0026thinsp;1.13, MIC, UHML, STR, Rd, +b, and SFI indices of the GR 2 samples changed synchronously with those of the GR 1 samples owing to the slope\u0026thinsp;=\u0026thinsp;0.90 to 1.00, whereas two trash indices (Area%, particle count) of the GR 2 samples decreased more than those of the GR 1 samples due to the slope\u0026thinsp;=\u0026thinsp;0.53 to 0.66. Meanwhile, absolute adjusted intercept (or ׀adjusted intercept׀) suggested a range of \u0026lt;\u0026thinsp;0.10 for MIC, UHML, STR, Rd, +b, and SFI, a range of between 0.10 and 0.30 for UI (-0.12), and a range of greater than 0.30 for two trash indices (0.37 to 0.39). In addition, MIC, UHML, STR, Rd, and +\u0026thinsp;b showed a high R\u003csup\u003e2\u003c/sup\u003e (\u0026gt;\u0026thinsp;0.85), followed by the UI and SFI (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.68 to 0.77) and the two trashes (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.41 to 0.56). Because the slope, ׀adjusted intercept׀, and R\u003csup\u003e2\u003c/sup\u003e in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e were from one seed cotton sample undergone two SG processing, ideally the slope and R\u003csup\u003e2\u003c/sup\u003e would be close to 1.0 while the ׀adjusted intercept׀ would be close to 0.0 when SG-dependent quality change was non-existent or minimal. Under this scenario, thresholds for the slope, ׀adjusted intercept׀, and R\u003csup\u003e2\u003c/sup\u003e would be considered to be 0.90 to 1.10, \u0026lt; 0.10, and \u0026gt;\u0026thinsp;0.85, respectively. This led to five HVI measurements (MIC, UHML, STR, Rd, +b) falling into these criteria. Hence, the ginning rate impacted fiber UI greatly and significantly, and also SFI and two trash components largely.\u003c/p\u003e\n\u003cp\u003eReasonably, some seed cotton samples would exhibit relatively apparent difference for any one of these HVI qualities between the two SG rates. As an example, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows a significant and strong correlation between fiber MIC from the GR 1 samples and those from the GR 2 samples (slope\u0026thinsp;=\u0026thinsp;0.97, ׀adjusted intercept׀ = 0.02, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.93). Four samples were observed to have a MIC absolute difference of \u0026gt;\u0026thinsp;0.20. Among these four samples, one (MIC\u0026thinsp;\u0026lt;\u0026thinsp;4.0) increased and the other three (MIC\u0026thinsp;\u0026gt;\u0026thinsp;4.0) decreased in MIC at GR 2 compared to GR 1. These MIC differences were checked against AFIS fineness, IFC, and MR values, and it was found that the differences were related with corresponding IFC differences negatively and moderately (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.19), with fineness differences positively and moderately (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.15), and with MR differences positively and weakly (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.10).\u003c/p\u003e\n\u003cp\u003eThe effect of ginning rate on UI is clearly visible in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, with relatively a larger UI reading at GR 2 than at GR 1. Higher UI values, resulted from higher average lengths among the GR 2 samples, could be due to a number of shorter fibers being blown out the lint collection cage at higher GR or being brushed away from the beard of fibers in HVI test. Five samples were found to have a UI increase of \u0026gt;\u0026thinsp;1.0 with ginning rate increasing. Further analysis revealed unclear effect of cultivar, crop years, and grown locations on distinctive UI difference in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eThe effect of blending on HVI measurements is compared in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. There were statistically significant differences for Rd and two trash indices between unblended and blended samples for each of two SG rates. During the blending process, non-lint materials were falling from the sample, causing the two trash reading reduction and the Rd value increasing apparently. For instance, particle count decreased from 81.14 to 40.16 for the GR 1 samples and from 73.10 to 45.14 for the GR 2 samples, whereas the Rd improved from 73.08 to 77.10 for the GR 1 samples and from 73.71 to 76.86 for the GR 2 samples. Although there was statistically significant difference for UI in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, there were insignificant differences for UI between unblended and blend samples in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eIndependent of ginning rate, MIC, UHML, and +\u0026thinsp;b values between unblended and blended samples showed a satisfactory slope of 0.88 to 1.04, an ׀adjusted intercept׀ of less than 0.10, and a R\u003csup\u003e2\u003c/sup\u003e of 0.91 to 0.97 (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Contrarily, Rd values indicated a moderate slope of 0.80 to 0.82, a poor ׀adjusted intercept׀ of around 0.23, and a R\u003csup\u003e2\u003c/sup\u003e of 0.80 to 0.92, whereas two trash indices exhibited both a low slope (0.44 to 0.59) and a low R\u003csup\u003e2\u003c/sup\u003e (0.39 to 0.65). In addition, UI, STR, and SFI from the GR 1 samples revealed an acceptable slope of 0.84 to 1.04, an ׀adjusted intercept׀ of 0.03 to 0.19, and an R\u003csup\u003e2\u003c/sup\u003e of 0.71 to 0.94, but these properties from the GR 2 samples became worse in the slope of 0.64 to 0.76, ׀adjusted intercept׀ of 0.20 to 0.30, and R\u003csup\u003e2\u003c/sup\u003e of 0.47 (except for UI with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.80 and STR with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.87). Therefore, blending procedure influenced fiber UI, STR, Rd, SFI, and two trash indices apparently.\u003c/p\u003e\n\u003cp\u003eOn the basis of two correlation lines between unblended and blended samples from two ginning rates, analysis of covariance (ANCOVA) in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e highlighted the statistically significant differences in both slope and intercept for STR, insignificant differences in slope but significant differences in intercept for Rd, SFI, and two trash indices, and insignificant differences in both slope and intercept for MIC, UHML, UI, and +\u0026thinsp;b. This observation implied statistically significant sample by ginning and by blending interactions for STR, Rd, SFI, Area%, and particle count, or statistically insignificant sample x ginning x blending interaction for the remaining HVI properties (MIC, UHML, UI, +b).\u003c/p\u003e\n\u003cp\u003eFigures \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e show the relationships between unblended and blended samples from two ginning rates for MIC and UI, respectively. Two correlation lines in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e resembled each other and went along with diagonal line satisfactorily, but it showed one GR 1 sample and one GR 2 sample with an MIC absolute difference of \u0026gt;\u0026thinsp;0.20 following sample blending. Although being similar to MIC in ANCOVA statistics (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 in both slope and intercept), UI property in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e suggested a different pattern of UI responding to blending process, underscoring the effect of blending on UI measurement. Three GR 1 samples and one GR 2 sample were observed to have UI increase of \u0026gt;\u0026thinsp;1.0. Although the difference of UI for the GR 1 samples or for the GR 2 samples was insignificant following the blending, there were a mean UI increase for both GR 1 samples (83.05 to 83.41) and GR 2 samples (83.56 to 83.68). Higher UI values might imply a loss of shorter fibers being spun away during the blending or being brushed away from the beard of fibers in HVI test.\u003c/p\u003e\n\u003ch3\u003eGinning rate and blending on fiber AFIS quality\u003c/h3\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e details the impact of the ginning rates on AFIS qualities from unblended samples between GR 1 and GR 2. Two SFC properties (SFC(w) and SFC(n)) indicated significant differences between 2 ginning rates and suggested a significant sample by ginning interaction.\u003c/p\u003e\n\u003cp\u003eAs given in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, only UQL(w) and L5%(n) revealed a good slope of 0.91 to 0.95, a least ׀adjusted intercept׀ of \u0026lt;\u0026thinsp;0.10, and a moderate R\u003csup\u003e2\u003c/sup\u003e of 0.73 to 0.76 among 20 AFIS properties, Comparatively, there were five of nine HVI measurements (MIC, UHML, STR, Rd, +b) with a slope of 0.90 to 1.10, an ׀adjusted intercept׀ of \u0026lt;\u0026thinsp;0.10, and a R\u003csup\u003e2\u003c/sup\u003e of \u0026gt;\u0026thinsp;0.85. Therefore, the ginning rate influenced nearly all AFIS measurements greatly but was less for UQL(w) and L5%(n) relatively. A distinctive difference between the 2 measurements is that HVI tests a bundle of fibers, while AFIS measures the individualized fibers.\u003c/p\u003e\n\u003cp\u003eCorrelations of selected AFIS qualities (UQL(w), MR, and fineness) from unblended samples between GR 1 and GR 2 are shown in Figs. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e, and \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e, respectively. Increase of ginning rate led to an UQL(w) increase of \u0026gt;\u0026thinsp;0.04 for two samples and an UQL(w) decrease of \u0026gt;\u0026thinsp;0.04 for two samples (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). Also, ginning rate altered MR in Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e, with a MR increase of \u0026gt;\u0026thinsp;0.02 for four samples having MR values\u0026thinsp;\u0026lt;\u0026thinsp;0.86 and a MR decrease of \u0026gt;\u0026thinsp;0.02 for one sample having a MR value around 0.90. In other words, high GR might have a possibility of producing a greater MR value when a sample\u0026rsquo;s MR was \u0026lt;\u0026thinsp;0.86. Like MR, AFIS fineness tended to be larger for lower fineness samples (\u0026lt;\u0026thinsp;150) at a higher GR (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e). However, there were four samples with fineness increase of \u0026gt;\u0026thinsp;6.0 and 3 samples with fineness decrease of \u0026gt;\u0026thinsp;6.0, and these 7 samples were distributed equally along the 45-degree diagonal line. This observation suggested a relatively larger effect of GR on MR than on fineness. The reason might be due to the fact that AFIS MR is calculated from the percentages of both mature fiber content (Ɵ \u0026ge; 0.50) and immature fiber content (Ɵ \u0026lt; 0.25) by measuring the fiber shape, whereas AFIS fineness is determined by the algorithm based on the shape and form of the fibers and also their specific weight.\u003c/p\u003e\n\u003cp\u003eStatistical significances for nep size, neps, SCN size, SCN count, total count, trash size, dust count, particle count, and visible foreign matter (VFM) difference between unblended and blended samples were noted for least one of two SG rates (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). In other words, blending process did not impact fiber AFIS length and maturity measurements significantly. SCN size showed very weak correlations (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.02 to 0.03) between unblended and blended samples for two ginning rates, and will not be discussed.\u003c/p\u003e\n\u003cp\u003eBlending of either GR 1 or GR 2 samples resulted in greater neps for blended samples than for unblended samples, for example, a neps increasing from 239.94 to 401.74 for the GR 1 samples and from 213.82 to 307.70 for the GR 2 samples. That is, mechanical blending of the GR 1 samples (slope\u0026thinsp;=\u0026thinsp;1.46) produced more neps than that of the GR 2 samples (slope\u0026thinsp;=\u0026thinsp;1.08). To understand the blending effect, the neps differences by subtracting neps values of the blended samples from those of the unblended ones were linked with fineness, IFC, and MR values of unblended samples. The neps differences were related with MR negatively and greatly (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.44), followed with fineness negatively (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.14) and with IFC positively (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.10) for the GR 1 samples. In contrast, the neps differences were not associated with MR (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.08), fineness (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.10), and IFC (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) among the GR 2 samples. Hence, blending caused more neps in low MR samples ginned at low ginning rate.\u003c/p\u003e\n\u003cp\u003eAll AFIS properties but neps from the GR 1 samples revealed a slope of 0.38 to 0.87, an ׀adjusted intercept׀ of 0.01 to 0.63, and a R\u003csup\u003e2\u003c/sup\u003e of 0.24 to 0.88, whereas those from the GR 2 samples suggested a slope range of 0.29 to 1.12, an ׀adjusted intercept׀ of 0.01 to 0.72, and a R\u003csup\u003e2\u003c/sup\u003e range of 0.08 to 0.94 (Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e). Clearly, blending of the GR 2 samples improved the measurement agreement for length and SFC indices, fineness, and MR with a fitting slope of 0.94 to 1.12 for the GR 2 samples compared to that of 0.55 to 0.87 for the GR 1 samples, accompanied by a reduced ׀adjusted intercept׀ of 0.01 to 0.13 for the GR 2 samples against that of 0.13 to 0.48 for the GR 1 samples, and by an enhanced R\u003csup\u003e2\u003c/sup\u003e of 0.73 to 0.94 for the GR 2 samples relative to that of 0.52 to 0.88 for the GR 1 samples. Therefore, the blending process influenced all AFIS properties completely.\u003c/p\u003e\n\u003cp\u003eANCOVA analysis revealed statistically significant differences in slope but insignificant differences in intercept for L(w) CV, UQL(w), L(n) CV, SFC(n), and fineness, and also insignificant differences in slope but significant differences in intercept for neps, IFC, total count, dust count, particle count, and VFM. This result implied statistically significant sample x ginning x blending interactions for neps, L(w) CV, UQL(w), L(n) CV, SFC(n), IFC, fineness, total count, dust count, particle count, and VFM. Other AFIS properties (nep size, SCN count, L(w), SFC(w), L(n), L5%(n), MR, and trash size) might not be affected by sample x ginning x blending interaction.\u003c/p\u003e\n\u003cp\u003eFigures \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e through \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e depict the correlations of selected AFIS qualities (UQL(w), MR, and fineness) between unblended samples and blended samples from GR 1 and GR 2 test, respectively. Compared to the GR 1 samples, the GR 2 samples indicated a good agreement for UQL(w) between unblended and blended samples with a desired slope, least intercept, and elevated R\u003csup\u003e2\u003c/sup\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e). Blending process of the GR 1 samples caused an UQL(w) increase of \u0026gt;\u0026thinsp;0.04 for four samples and a UQL(w) decrease of \u0026gt;\u0026thinsp;0.04 for three samples, while the process of the GR 2 samples did not induce an UQL(w) difference of \u0026gt;\u0026thinsp;0.04 for any sample. The UQL(w) differences were correlated positively and greatly with fineness (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.34) and MR (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.25) but negatively and weakly with IFC (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.20) for the GR 1 samples, whereas the differences were not connected with fineness (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), MR (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and IFC (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the GR 2 samples. Therefore, blending raised UQL(w) values for high fineness samples ginned at low ginning rate.\u003c/p\u003e\n\u003cp\u003eSimilarly, blending improved MR and fineness regression statistics more for the GR 2 samples than for the GR 1 samples (Figs. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e). Six GR 1 samples were noted to have a MR increase of \u0026gt;\u0026thinsp;0.02 and six GR 2 samples were to have a MR increase of \u0026gt;\u0026thinsp;0.02, implying the potential of causing greater MR values for some samples when they were blended (Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e). MR alike, fineness increases of \u0026gt;\u0026thinsp;6.0 were observed among ten GR 1 samples and nine GR 2 samples, but fineness decreases of \u0026gt;\u0026thinsp;6.0 were noted on two GR 1 samples (Fig. \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e). This result suggested increased fineness readings of some samples if they were blended.\u003c/p\u003e\n\u003ch2\u003eGinning rate and blending on the correlations of fiber HVI against AFIS quality\u003c/h2\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e outlines the effects of both GR and blending on the correlations between HVI and relevant AFIS qualities from unblended samples to those blended. Regardless of GR setting, HVI UHML showed the greatest correlations with UQL(w) and L5%(n) (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.83 to 0.94), followed with L(w) (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.41 to 0.70), and the least with L(n) (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.02 to 0.13) for the unblended samples. This pattern remained unchanged among the blended samples, except for the two pairs of UHML vs. UQL(w) and UHML vs. L5%(n) from the GR 1 samples with a lower R\u003csup\u003e2\u003c/sup\u003e (=\u0026thinsp;0.64). Comparison of HVI UHML to AFIS lengths is of great interest, since HVI UHML determines the fiber length from the fibrogram of scanning a fiber beard that many short fibers may not protrude far enough to be scanned, while AFIS lengths characterize the complete within-sample distribution of all single fibers by weight and by number. Similar to HVI UHML that reflects the average length by number of the longer half of the fibers by weight, AFIS UQL(w) characterizes the fiber lengths of the longer 25% of all fibers by weights and L5%(n) measures the fiber lengths of the longer 5% of all fiber by number. Therefore, a high R\u003csup\u003e2\u003c/sup\u003e between HVI UHML and AFIS UQL(w) or L5%(n) than between HVI UHML and AFIS L(w) or L(n) is expected. Overall, HVI UHML had a greater correlation with AFIS L(w) (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.31 to 0.70) than with AFIS L(n) (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.02 to 0.13) without the consideration of ginning and blending effects. The tendency was similar to a reported Pearson correlation coefficient (\u003cem\u003eR\u003c/em\u003e) of 0.923 between HVI UHML and AFIS L(w) as well as of 0.823 between HVI UHML and AFIS L(n) (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.02 to 0.13) for samples with HVI UHML from 0.96 to 1.20 inch (van der Sluijs et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eANCOVA result of unblended samples indicated statistically significant differences in intercept for the four pairs (UHML vs. L(w), UHML vs. UQL(w), UHML vs. L(n), and UHML vs. L5%(n)), while those of blended samples implied insignificant differences statistically in both slope and intercept for any pairs. As an example, Fig. \u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003e depicts two correlation lines of HVI UHML vs. AFIS UQL(w) from the GR 1 and GR 2 samples between unblended and blended process, with a notable worsening R\u003csup\u003e2\u003c/sup\u003e from 0.83 to 0.64 for the GR 1 samples following the blending process.\u003c/p\u003e\n\u003cp\u003eIndependent of both GR and blending, HVI SFI was observed to have moderate correlations with SFC(w) (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.12 to 0.29) but weak correlations with SFC(n) (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.07 to 0.19), in which the pattern resembles a report between HVI SFI against SFC(w) (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.777) and against SFC (n) (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.655) (van der Sluijs et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Meanwhile, a decreasing R\u003csup\u003e2\u003c/sup\u003e between HVI SFI and SFC(w) or SFC(n) echoes a reduced R\u003csup\u003e2\u003c/sup\u003e from HVI UHML vs. AFIS L(w) (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.31 to 0.70) to HVI UHML vs. AFIS L(n) (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.02 to 0.13). ANCOVA result showed statistically insignificant differences in slope and in intercept for four pairs excepting one pair (SFI vs. SFC(w)) of unblended samples, which indicated a significant difference in intercept.\u003c/p\u003e\n\u003cp\u003eRelatively, HVI MIC showed great correlations with fineness (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.61 to 0.92), followed with IFC (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.56 to 0.72) and MR (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.53 to 0.71). In general, GR or blending did not impact the relationships of any pairs clearly, except that blending process reduced the MIC vs. fineness correlation of GR 1 samples from R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.92 to 0.61, which was accompanied by a statistically significant difference in slop (ANCOVA \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e\n\u003cp\u003eOpposite to L(w) vs. L(n) relationship possessing a moderate R\u003csup\u003e2\u003c/sup\u003e of 0.62 to 0.72, two pairs (UQL(w) vs. L5%(n) and SFC(w) vs. SFC(n)) showed a strong R\u003csup\u003e2\u003c/sup\u003e of \u0026gt;\u0026thinsp;0.95 to 0.98. Both GR and blending did not affect the correlations of these pairs obviously, as the data were presented by the weight or the number of the fibers in a sample from one measurement.\u003c/p\u003e\n\u003cp\u003eIn general, a few effects of both GR and blending on the correlations between HVI and relevant AFIS qualities were noticed. For instance, three pairs (UHML vs. UQL(w), UHML vs. L5%(n), and MIC vs. fineness) of lower GR samples showed a reduced R\u003csup\u003e2\u003c/sup\u003e from unblended to blended samples. Four pairs (UHML vs. L(w), UHML vs. UQL(w), UHML vs. L(n), and UHML vs. L5%(n)) suggested statistically significant differences in intercept among unblended samples, while one pair (MIC vs. fineness) indicated statistically significant difference in slope among blended samples. Especially, a R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.90 might hint the consistency of measuring fiber UHML or UQL(w) or L5%(n) quality on higher GR samples from HVI or AFIS independent of fiber blending.\u003c/p\u003e\n\u003ch3\u003eGinning rate and lint turnout\u003c/h3\u003e\n\u003cp\u003eThe lint turnout for the GR 1 samples was in good agreement with that for the GR 2 samples (a slope of 1.00, an adjusted intercept of 0.05, and a R\u003csup\u003e2\u003c/sup\u003e of 0.83), and there was no statistically significant difference (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.47) between the two (Fig. \u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003e). Although the average lint turnout (=\u0026thinsp;41.9%) of the GR 1 samples was similar to that (=\u0026thinsp;41.6%) of the GR 2 samples, eight samples from the GR 2 samples had a lint turnout deduction of \u0026gt;\u0026thinsp;1.0% compared to the GR 1 samples, whereas one of four Deltapine samples from the GR 2 samples had a lint turnout increase of \u0026gt;\u0026thinsp;1.0%. The lint turnout (41.6% ~ 41.9%) from mechanically harvested seed cottons in this study was a little lower than those of 41.9% ~ 43.4% from 30 clean bolls each of three seed cotton Upland varieties (Rodgers et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study compared fiber HVI and AFIS quality of diversified lint ginned with a laboratory 10-saw gin at two GRs (lower GR 1 vs. higher GR 2) and then blended with a laboratory mechanical blender. With GR increasing, HVI results for UI, SFI, and two trashes and also AFIS results for nearly all properties were impacted, with statistically significant effects on HVI UI and on 2 AFIS SFC parameters. For example, higher GR process enhanced the HVI UI property significantly, however it did not improve UHML in the same way. Also, GR elevation might generate a greater MR or fineness value for samples with lower MR or fineness, and also could impact MR property more than fineness property.\u003c/p\u003e \u003cp\u003eThe effect of fiber blending on HVI UI, STR, Rd, SFI, and two trashes as well as on all AFIS results was observed, with significant effects on HVI Rd and two trashes as well as without significant impact on AFIS length and maturity measurements. Although blending did not cause the difference of HVI UI significantly for any of 2 GRs, there was a little UI increase following the blending of samples. Blending caused neps increasing more for low GR samples than for high GR samples, whereas the process improved MR or fineness agreement more for high GR samples than for low GR samples.\u003c/p\u003e \u003cp\u003eWhen HVI and AFIS data was correlated to determine if relevant properties from HVI and AFIS measurements responded similarly to ginning and blending process, it was found that three pairs (UHML vs. UQL(w), UHML vs. L5%(n), and MIC vs. fineness) of lower GR samples showed worsening correlations from unblended to blended samples. Relative to one pair (MIC vs. fineness) of blended samples revealing statistically significant difference in slope, other four pairs (UHML vs. L(w), UHML vs. UQL(w), UHML vs. L(n), and UHML vs. L5%(n)) of unblended samples indicated statistically significant differences in intercept.\u003c/p\u003e \u003cp\u003eOverall effect of both ginning and blending process suggested statistically significant interactions for HVI STR, Rd, SFI, and two trashes, and also for AFIS neps, L(w) CV, UQL(w), L(n) CV, SFC(n), L5%(n), IFC, fineness, total count, dust count, particle count, and VFM. Despite the observation, laboratory saw gin or other ginning methods with or without blending should continue to be useful tools for exploring sample differences from fiber HVI and/or AFIS properties as long as an identical procedure is applied to all samples.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors wish\u0026nbsp;to acknowledge two ARS Cotton Ginning laboratories (New Mexico and Mississippi) in providing seed cotton samples, Ms M Dunn in cotton ginning, and Ms H King in fiber HVI and AFIS measurement. We also thank Mr\u0026nbsp;D McAlister\u0026nbsp;of ARS SRRC Senior Textile Advisor and Dr\u0026nbsp;C Armijo\u0026nbsp;of ARS Cotton Ginning Research for providing expertise insights for improving the quality of the manuscript. Mention of a product or specific equipment does not constitute a guarantee or warranty by the U.S. Department of Agriculture and does not imply its approval to the exclusion of other products that may also be suitable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDelhom C conceived the research. Liu Y collected cotton samples and conducted data analysis. Both authors prepared the manuscript and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the USDA-ARS Research Project # 6054-44000-080-00D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used or generated are included in the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors do not have competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e USDA, ARS, Southern Regional Research Center (SRRC), Cotton\u0026nbsp;Quality and Innovation Research Unit, New Orleans, LA 70124, USA\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e USDA, ARS, Sustainable Water Management Research Unit, Stoneville, MS 38776, USA\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAnthony, WS. The effect of gin machinery on measurement of high volume instrument color and trash of cotton. Trans ASAE. 1994; 37:373-380.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eArmijo, CB, Tumuluru, JS, Delhom CD, Whitelock DP, Funk PA, Wanjura JD, Kothari N, and Martin VB. Investigating fiber quality from tabletop and commercial scale saw and roller gin stands. Proceeding of 2023 Beltwide Cotton Conferences. 2023; pp. 490-491.\u003c/li\u003e\n \u003cli\u003eBlake, CD, Whitelock DP, Funk PA, Armijo CB, and Buser MD. The impact of ginning rate on fiber and seed quality. Appl Eng Agric. 2022; 38:9-14.\u003c/li\u003e\n \u003cli\u003eBoykin, JC. Small sample techniques to evaluate cotton variety trials. J Cotton Sci. 2008; 12:16\u0026ndash;32.\u003c/li\u003e\n \u003cli\u003eBoykin JC, Whitelock DP, Armijo CB, Buser MD, Holt GA, Valco TD, Findley DS, Barnes EM, and Watson MD. Predicting fiber quality after commercial ginning based on fiber obtained with laboratory-scale gin stands. J Cotton Sci. 2010; 14:34\u0026ndash;45.\u003c/li\u003e\n \u003cli\u003eBradow J, Davidonis G. Quantitation of fiber quality and the cotton production-processing interface: a physiologist\u0026apos;s perspective. J Cotton Sci. 2000; 4:34-64.\u003c/li\u003e\n \u003cli\u003eByler RK, Delhom CD. Comparison of saw ginning and high-speed roller ginning with different lint cleaners of mid-south grown cotton. Appl Eng Agric. 2012; 28:475-482.\u003c/li\u003e\n \u003cli\u003eCotton Incorporated. The classification of cotton. Cotton Incorporated, Cary, NC. 2018.\u003c/li\u003e\n \u003cli\u003eDelhom CD, Kelly B, Martin V. Physical properties of cotton fiber and their measurement. In: Fang DD, editors. Cotton Fiber: Physics, Chemistry and Biology. Cham, Switzerland: Springer; 2018. p 41-73.\u003c/li\u003e\n \u003cli\u003eDelhom CD, Knowlton J, Martin VB, Blake C. The classification of Cotton. J Cotton Sci. 2020; 24:189\u0026ndash;196.\u003c/li\u003e\n \u003cli\u003eHardin IV RG, Barnes EM, Valco TD, Martin VB, Culp DM. Effects of gin machinery on cotton quality. J Cotton Sci. 2018; 22:36-46.\u003c/li\u003e\n \u003cli\u003eHinze L, Scheffler J, Thompson A, Hague S, Udall J, Kothari N. Comparison of fiber quality parameters from laboratory ginning systems used in cotton breeding programs. Proceeding of 2023 Beltwide Cotton Conferences. 2023; p 527.\u003c/li\u003e\n \u003cli\u003eHinze L, Scheffler J, Thompson A, Hague S, Udall J, Kothari N. Comparison of fiber quality parameters from laboratory ginning systems used in cotton breeding programs. Proceeding of 2024 Beltwide Cotton Conferences. 2024; p 431.\u003c/li\u003e\n \u003cli\u003eHughs SE, Armijo CB. Impact of gin saw-tooth design on fiber and textile processing quality. J Cotton Sci. 2015; 19:27-32.\u003c/li\u003e\n \u003cli\u003eHolt GA, Laird JW. Initial fiber quality comparisons of the power roll gin stand to three different makes of conventional gin stands. Appl Eng Agric.2008;24: 295-299.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKelly C, Hequet E, Dever J. Interpretation of AFIS and HVI fiber property measurements in breeding for cotton fiber quality improvement.\u0026nbsp;J Cotton Sci. 2012; 16:1-16.\u003c/li\u003e\n \u003cli\u003eKelly B, Abidi N, Ethridge D, Hequet EF. Fiber to fabric. In Fang DD, Percy RG, editors. Cotton, 2nd edition. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, 2015. p 665-744.\u003c/li\u003e\n \u003cli\u003ePaudel D, Hequet E, Abidi N. Evaluation of cotton fiber maturity measurements. Ind Crop Prod. 2013; 45:435\u0026ndash;441.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRobert KQ, Dunn MC, Cosenza FA. Blending of cotton fiber samples for length measurement. Proceeding of 2005 Beltwide Cotton Conferences. 2005. p 2718-2730.\u003c/li\u003e\n \u003cli\u003eRobertson WC, Roberts BA. Integrated crop management for cotton production in the 21\u003csup\u003est\u003c/sup\u003e century. In Wakelyn PJ, Chaudhry MR, editors. Cotton: Technology for the 21st Century. Washington DC, USA: International Cotton Advisory Committee. 2010. p 63-97.\u003c/li\u003e\n \u003cli\u003eRodgers J, Fortier C, Delhom C, Cui X. \u0026nbsp;Laboratory ginning and blending impacts on cotton fiber micronaire measurements. AATCC J Res. 2015; 2:1-7.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003evan der Sluijs MHJ, Delhom CD, Martin VB. Assessment of cotton fibre length measurement methods. J Text I. 2021; 112:1377\u0026ndash;1389.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWhitelock DP, Buser MD, Armijo CB, Hughs SE. The impact of historical gin stand technologies on cotton fiber and seed quality. Appl Eng Agric. 2019; 35:775-785. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 2-6 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cotton-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cotn","sideBox":"Learn more about [Journal of Cotton Research](https://jcottonres.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/cotn/default.aspx","title":"Journal of Cotton Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"AFIS, blending, cotton fiber, HVI, laboratory saw gin, ginning rate","lastPublishedDoi":"10.21203/rs.3.rs-6222116/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6222116/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSmall scale research or individual seed cotton samples from breeders and geneticists are ginned using laboratory saw or roller gins. The objective of this study was to examine the effects of two different laboratory saw ginning (SG) rates and subsequent mechanical blending on fiber high volume instrument (HVI) and advanced fiber information system (AFIS) quality measurement. Seed cottons from diverse Upland cotton cultivars, years, and locations were evaluated. As SG rate was increased, HVI results for uniformity index (UI), short fiber index (SFI), and two non-lint parameters (Area%, particle count) and also AFIS results for some properties were impacted, with significant effects on HVI UI and two AFIS short fiber content (SFC) indices. Apparent fiber blending impacts were observed for HVI UI, strength (STR), Rd, SFI, and two trashes as well as for all AFIS parameters, with significant effects on HVI Rd and two trashes and all AFIS parameters except the length and maturity measurements. A combination of ginning and blending process indicated statistically significant interactions for HVI STR, Rd, SFI, and two trashes, and also for AFIS neps, L(w) CV, UQL(w), L(n) CV, SFC(n), L5%(n), immature fiber content (IFC), fineness, total count, dust count, particle count, and visible foreign matter (VFM). Further analysis implied a few impacts of ginning and blending on correlations between HVI and relevant AFIS qualities. Despite the observation, laboratory saw gin and blending methods should continue to be a practical tool to study the differences when all samples are processed identically.\u003c/p\u003e","manuscriptTitle":"Investigation of laboratory saw ginning and blending on cotton fiber quality measurement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-19 08:43:38","doi":"10.21203/rs.3.rs-6222116/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2025-06-05T03:46:48+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-03-17T13:41:04+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-17T13:27:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-14T05:20:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cotton Research","date":"2025-03-13T13:44:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cotton-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cotn","sideBox":"Learn more about [Journal of Cotton Research](https://jcottonres.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/cotn/default.aspx","title":"Journal of Cotton Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"85189732-9da1-434a-bcf8-59cb76035375","owner":[],"postedDate":"March 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:04:04+00:00","versionOfRecord":{"articleIdentity":"rs-6222116","link":"https://doi.org/10.1186/s42397-025-00238-w","journal":{"identity":"journal-of-cotton-research","isVorOnly":false,"title":"Journal of Cotton Research"},"publishedOn":"2025-12-02 15:58:17","publishedOnDateReadable":"December 2nd, 2025"},"versionCreatedAt":"2025-03-19 08:43:38","video":"","vorDoi":"10.1186/s42397-025-00238-w","vorDoiUrl":"https://doi.org/10.1186/s42397-025-00238-w","workflowStages":[]},"version":"v1","identity":"rs-6222116","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6222116","identity":"rs-6222116","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}