Life Cycle Assessment – Evaluating the Physical and Sensory Quality of Bondowoso Arabica Coffee through Precision Fermentation

Life Cycle Assessment – Evaluating the Physical and Sensory Quality of Bondowoso Arabica Coffee through Precision Fermentation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Life Cycle Assessment – Evaluating the Physical and Sensory Quality of Bondowoso Arabica Coffee through Precision Fermentation Asmak Afriliana, Nafi Ahmad, Triana Lindriati, Novizar Nazir, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8983516/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Coffee production is vital for the economic activities of smallholders for annual income, Arabica Coffee Bondowoso, Indonesia. Smallholder coffee farms can increase prices and break the long sales chain with quality specialty coffee. This study aims to understand in-depth precision fermentation as an evaluation of the physical and sensory quality of Green Beans (GB). A Life Cycle Assessment (LCA) was applied to evaluate the Global Warming Potential (GWP). Observation and experimentation were chosen for the coffee process: anaerobic. Primary data were combined with secondary data calculations using the ReciPe 2016 method, Simapro v9.3.03. The results of this study provide coffee category grade two, including coffee scores of 84.25, respectively, for anaerobic. GWP from processed Cherry Beans (CB) to Coffee Powder (CP) are 1.39 kg CO2-eq for one kg CP, respectively. Keywords: Life cycle assessment (LCA), Global warming potential (GWP), anaerobic, precision fermentation, sensory quality. Earth and environmental sciences/Environmental sciences Biological sciences/Microbiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Coffee production underpins rural livelihoods across many tropical countries and remains especially critical for smallholders, who contribute a substantial proportion of global supply and rely on coffee sales as a primary source of household income. In Indonesia, coffee is an export-oriented strategic commodity, and current development efforts increasingly emphasize quality improvement at both the farm and post-harvest stages to enable access to higher-value specialty markets. Indonesia cultivates both Arabica ( Coffea arabica ) and Robusta ( Coffea canephora ). While Robusta typically dominates national production in volume, Arabica contributes a smaller share yet provides higher economic value because it is closely associated with specialty-grade characteristics and origin-based differentiation. Arabica is generally grown at higher elevations (commonly ≥ 700 m a.s.l.), whereas Robusta is more tolerant of lower elevations and warmer conditions, resulting in distinct production landscapes, harvesting calendars, and post-harvest processing choices. The commercialization of specialty coffee is closely linked to standardized sensory evaluation and market systems that reward consistent cup quality. The Specialty Coffee Association (SCA) uses a 100-point cupping framework in which coffees scoring ≥ 80 points are commonly classified as “specialty.” High-performing lots may further enter premium competitions and auctions such as the Cup of Excellence (COE), which applies multi-stage sensory screening and recognizes top-scoring coffees with substantial market premiums. In practice, participation in COE-type mechanisms can motivate farmers and processors to implement stricter cherry selection, improved defect control, and better traceability, thereby discouraging quality mixing and supporting a shift from commodity-grade coffee toward differentiated specialty lots. This pressure to meet international benchmarks is intensified by the global scale of coffee production; FAO records indicate that coffee is produced in more than 80 producing countries and territories, highlighting the strong competition in quality-based export markets. Post-harvest processing is a major determinant of final cup quality because it regulates the chemical and biochemical transformations that occur between harvest and drying. Broadly, coffee is processed using wet (washed) or dry (natural) methods, and both approaches can produce high-quality products depending on cultivar, ripeness, processing hygiene, and environmental conditions. Fermentation—whether spontaneous or controlled—is particularly influential because microorganisms modify sugars, acids, and aroma precursors in the mucilage and bean surface environment, which later shape the sensory attributes after roasting. Recent studies demonstrate that manipulating fermentation duration (including extended fermentation up to ~ 96 h), oxygen exposure (anaerobic or self-induced anaerobiosis), and the use of inoculation/starter cultures can generate distinct chemical profiles and sensory outcomes. Consequently, “precision fermentation” has emerged as a promising concept in specialty processing, aiming to improve reproducibility by controlling key parameters such as time, temperature, and mass transfer. Anaerobic fermentation performed in a controlled fermentor is especially important because it converts fermentation from a highly variable, artisanal step into a standardized and repeatable unit operation. Conventional anaerobic-style processing (e.g., sealed bags or drums under ambient conditions) often experiences uncontrolled temperature changes, uneven contact between mucilage and microbial communities, inconsistent oxygen exclusion, and variable contamination from the surrounding environment. These factors influence microbial succession and metabolic pathways, thereby affecting the formation of organic acids, alcohols, and aroma-active intermediates. As a result, sensory outcomes may vary markedly between batches, and the risk of defects—such as over-fermentation, unbalanced acidity, harsh/solvent-like notes, or undesirable “ferment” characteristics—may increase. Importantly, evidence from fermentation research indicates that relatively small deviations in fermentation time and temperature can meaningfully shift volatile composition and aroma profiles, reinforcing the need for tight operational control when targeting specialty-grade consistency. A fermentor-based approach enables control of three parameters that are particularly decisive in anaerobic coffee processing: (i) oxygen management, (ii) temperature regulation, and (iii) mixing/agitation. First, reliable oxygen limitation stabilizes anaerobic conditions and reduces uncontrolled microbial contamination, improving the predictability of fermentation trajectories. Second, temperature control is essential because fermentation kinetics are temperature dependent; without regulation, accelerated reactions can drive rapid acidification and excessive metabolite accumulation, increasing the likelihood of defects. Third, controlled mixing improves mass transfer and batch uniformity by reducing local gradients (e.g., hotspots, stagnant zones, uneven mucilage breakdown), which helps ensure consistent biochemical conversion across the entire batch. In addition to quality benefits, a closed and controlled fermentor strengthens environmental assessment because it enables systematic monitoring of process outputs, including gas production, and facilitates a more reliable mass-balance for life cycle inventory development. Moreover, improving coffee quality must be evaluated alongside environmental performance. Coffee processing can generate high-strength wastewater and emissions, and unmanaged by-products may contribute to local pollution as well as climate impacts. Coffee effluents often contain high organic loads (reflected in elevated COD and BOD) and suspended solids, which can degrade receiving water bodies if discharged without adequate treatment. From a climate perspective, energy use, wastewater handling (including potential methane generation under anaerobic conditions), and by-product management can significantly influence Global Warming Potential (GWP) outcomes in Life Cycle Assessment (LCA). Anaerobic streams may also provide mitigation opportunities if captured and valorized (e.g., through biogas recovery); however, this requires measurement-based inventory data, transparent assumptions, and appropriate system boundaries. In this study, Arabica coffee was selected because it is more strongly associated with the specialty segment and typically commands higher price premiums than Robusta when quality attributes (floral/fruity notes, acidity balance, and aroma complexity) are achieved consistently. Arabica is also generally more sensitive to post-harvest processing conditions, meaning that variations in fermentation temperature, duration, oxygen exposure, and mixing can lead to pronounced changes in organic acid formation, volatile precursor development, and ultimately cup profile. This sensitivity is advantageous for process research because it allows the effect of controlled fermentation to be detected more clearly through cup testing and quality grading. In addition, Arabica in Indonesia is commonly cultivated at higher altitudes, where cooler conditions and slower cherry maturation can support higher sensory potential, but this potential can be lost if fermentation is uncontrolled or inconsistent. Therefore, using Arabica provides a relevant and high-impact case to test whether a controlled fermentor can deliver repeatable specialty-quality improvements while also enabling more reliable measurement of emissions and wastewater characteristics for LCA and GWP accounting. 2. Materials and Methods 2.1 Study area The study was carried out with the Bondowoso Coffee Group in Pakis Village, Bondowoso Regency, East Java Province, Indonesia. The plantations were located at 1,048–1,400 m above sea level (latitude − 7.9231361, longitude 113.708889), representing a typical upland Arabica production zone. 2.2 Raw material selection Fully ripe red cherries were collected during the Arabica harvest season (May–June 2025) from plantations managed by the Bondowoso Coffee Group in the Argopuro Mountain region. The coffee trees in the study area have been cultivated for approximately 33 years. Cherries were manually sorted to remove visible defects (e.g., insect damage, mold, broken skin, and underripe/overripe fruit). The soluble solids content of cherry juice ranged from 11% to 17% °Brix, measured using an automatic temperature-compensated (ATC) optical refractometer. 2.3 Processing design and treatments Post-harvest processing was designed as controlled anaerobic fermentation in a closed fermentor followed by washing and drying. Freshly harvested ripe cherries were pulped to obtain coffee beans with mucilage (CB). The pulped CB were transferred into the fermentor and fermented for 96 h under anaerobic conditions. A controlled water addition was applied to support uniform fermentation and facilitate mucilage breakdown prior to subsequent washing and drying. Three experimental groups were evaluated: (i) control (no fermentation), (ii) anaerobic fermentation at 30°C, and (iii) anaerobic fermentation at 35°C. 2.4 Fermentor system A 5 kg per batch fermentation system was developed through collaborative research between the University of Jember and PT SAS (Jember), supported by BRIN funding [ 21 ]. The fermentor was equipped with an automatic stirrer (≈ 25 rpm), a water-jacket heater for temperature regulation (30–35°C) [ 22 – 24 ], and a radiator-based cooling unit to prevent overheating when jacket temperature exceeded ~ 36–40°C. Fermentation pH was monitored as an indicator of process progression [ 9 ]. Electricity demand was measured and reported as kWh per hour and per batch (Fig. 2 ). 2.5 Physical quality assessment (SNI) Green bean (GB) grade was determined using the Indonesian National Standard (SNI) defect-point system [ 18 ]. Approximately 300 g of beans were sampled after sorting. Defect values were summed to obtain a total defect score, which was then used to classify the coffee into Grades 1–6 (Table 1 ). Table 1 Defect parameter score SNI. Grade Quality Defect Parameters Score Grade 1 11 Grade 2 12–25 Grade 3 26–44 Grade 4 45–80 Grade 5 81–150 Grade 6 151–225 2.6 Sensory evaluation (SCA/CQI) Sensory evaluation was performed at the Indonesian Coffee and Cocoa Research Institute (ICCRI) [ 2 ] following the SCA cupping protocol and evaluated by at least two certified Q Arabica Graders [ 17 ]. All samples were roasted to medium roast (Agtron 55) [ 20 ]. Attribute scores and total score were recorded, including any taint/defect notes. 2.7 Gas and wastewater measurements Fermentation headspace gas was captured and analyzed by gas chromatography (GC) at the University of Jember to determine CO₂ and CH₄ concentrations. While liquid effluent from fermentation process was measured chemical oxygen demand (COD), biochemical oxygen demand (BOD), and total suspended solid (TSS) [ 11 ]. 2.8 Life cycle assessment The functional unit was 1 kg of coffee powder (CP). Primary data from observations and experiments were combined with secondary background data in SimaPro v9.3.0.3. Impact assessment was performed using the ReCiPe 2016 Midpoint (H) method [ 19 , 20 ]. Electricity consumption was modeled using the local grid supply. Process-stage energy demand is summarized in Table 2 , and the mass balance is summarized in Table 3 . Table 2 Energy demand at every stage* Materials Arabica Process Flow Processing Anaerobic Motor Cycle (Petrol), litre 0.25 Washing Cherry Beans, litre 5 Depulping, kWh 0.06 Fermentor, kWh 2.13 Dehulling, kWh 0.04 Roasting Coffee, kWh 0.44 Roasting Coffee Skin, kWh - Grinding Coffee, kWh 0.19 *Data were measured. Table 3 Mass balance by processing method for Bondowoso Arabica coffee* Stages process Anaerobic Cherry Beans, kg 5 Depulping, kg 2.5 Inject water, kg 2.3 Fermentation, kg 4.8 Drying CB/Pulp, kg 2.3 Dehulling, kg 2.3 Hand Sorting, kg 2.0 Roasting GB, kg 1.4 Grinding, kg 1.4 *Data were measured. 3. Results and Discussion 3.1 Physical processing and green bean defects On-farm and post-harvest handling influence bean integrity and defect formation. Field-related defects can arise from insect damage, notably the coffee berry borer (Hypothenemus hampei), which can reduce bean density and promote spoilage [ 25 ]. Processing and storage can also introduce defects such as cracked beans, parchment fragments, and discoloration associated with improper moisture control. Based on SNI grading, the final defect score for the anaerobic sample was 13.5, corresponding to Grade 2 (Table 4 ). Maintaining Grade 2 (defect score 12–25) reduces the likelihood of taint/defect attributes during cupping and supports specialty market targeting. Table 4 Defect score analysis. No Type of Defect (Seed) Defect score QS An SV An 1 1 Black seed 1 0 0 2 1 Half seed 0.5 1 0.5 3 1 Cracked black 0.5 0 0 4 1 Dried cherry 1 0 0 5 1 Dark brown 0.25 20 5 6 1 HS big-sized 1 0 0 7 1 HS medium-sized 0.5 0 0 8 1 HS small-sized 0.2 0 0 9 1 Parch skin seed 0.5 0 0 10 1 Parch big-sized 0.5 2 1 11 1 Parch medium-sized 0.2 0 0 12 1 Parch small-sized 0.1 0 0 13 1 Cracked seed 0.2 20 4 14 1 Pale seed 0.2 0 0 15 1 Hole seed 0.1 10 1 16 1 Hole (more than 1) 0.2 5 1 17 1 Freckles seed 0.1 10 1 18 1 Branch big-sized 5 0 0 19 1 Branch medium-sized 2 0 0 20 1 Branch small sized 1 0 0 Final defect score 13.5 Quantity seeds = QS; Score value = SV; Anaerobic = An 3.2 Sensory quality Cupping results are summarized in Table 5 , with descriptive notes in Table 6 . The 35°C treatment achieved the highest total score (84.50), with improved balance and sweetness-associated descriptors relative to the control and 30°C treatment. These results align with reports that controlled anaerobic fermentation and inoculation can yield fruit-forward profiles and specialty-level scores [ 26 , 27 ]. Because SCA commonly classifies scores of 80–84.99 as ‘Very Good’ and ≥ 85 as ‘Excellent’, the coffees in this study fall within the Very Good–Specialty range [ 17 ]. Table 5 Sensory quality attributes for Bondowoso Arabica coffee* Cup test Sample Atributes Control 30 0 C 35 0 C Fragrance 7.63 7.50 7.88 Flavor 7.50 7.50 7.88 Aftertaste 7.50 7.50 7.75 Acidity 7.50 7.38 7.63 Body 7.50 7.38 7.88 Uniformity 10 10 10 Balance 7.50 7.38 7.75 Clean Cup 10 10 10 Sweetness 10 10 10 Overall 7.50 7.38 7.50 Total Score 82.63 82.00 84.50 *Data were measured from the panelist ICCRI Table 6 Total score, classification, and description of Bondowoso Arabica Processing Compile Quality Notes Method Score Classification Description Control 82.63 Excellent-Specialty Toasty, nutty, soybean aroma, grassy, spicy-chili, brown sugar 30 0 C 82.00 Excellent-Specialty Nutty, soybean aroma, grassy, potato 35 0 C 84.50 Excellent-Specialty Brown sugar, nutty, honey, spicy-chili-honeyed-tea rose, black tea *Data were measured from panelist ICCRI 3.3 Emissions and wastewater characteristics Gas production profiles (Fig. 5 ) showed peak CO₂ on day 2 (≈ 34,550 ppm) followed by a decline by day 4 (≈ 11,340 ppm). CH₄ was detected at lower concentrations (≈ 1.96 ppm on day 1 to ≈ 3.2 ppm on day 3), but was included due to its higher climate impact per unit mass. Wastewater COD, BOD, and TSS (Fig. 6) were measured and included in the life cycle inventory to represent effluent burden [ 11 ]. 3.4 GWP results and hotspot analysis The modeled GWP from CB to CP was 1.39 kg CO₂-eq per 1 kg CP (Table 7 ). Electricity consumption for fermentor operation (2.13 kWh per batch) was a major contributor, accounting for 0.67 kg CO₂-eq of the total, while the remaining stages contributed 0.72 kg CO₂-eq. These results indicate a quality–environment trade-off: controlled fermentation improves reproducibility and sensory performance but can increase GWP via electricity demand. Optimizing insulation, heating efficiency, batch scheduling, and using lower-carbon electricity could reduce this hotspot. Table 7 Results of this study on the anaerobic process. 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(NIMS)","correspondingAuthor":false,"prefix":"","firstName":"Endar","middleName":"","lastName":"Hidayat","suffix":""},{"id":597894203,"identity":"fbd39f38-d689-48c9-aaf9-fe4f66d3d981","order_by":8,"name":"Ning Puji","email":"","orcid":"","institution":"University of Jember","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Puji","suffix":""}],"badges":[],"createdAt":"2026-02-27 04:55:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8983516/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8983516/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104399921,"identity":"1c4c6200-b22b-46ab-8651-9b7421d0b939","added_by":"auto","created_at":"2026-03-11 12:08:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68896,"visible":true,"origin":"","legend":"\u003cp\u003eScope of coffee processing for this study.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8983516/v1/56079aba63b11f62e79a361c.jpg"},{"id":103724553,"identity":"81dbe2cb-2f4a-466f-b182-96abf8d705a6","added_by":"auto","created_at":"2026-03-02 08:00:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66917,"visible":true,"origin":"","legend":"\u003cp\u003eElectricity consumed within one hour using coffee fermentor.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8983516/v1/bcf024db1450809077038103.jpg"},{"id":103724554,"identity":"efc1fadd-fd9d-4c76-a829-8ef860373871","added_by":"auto","created_at":"2026-03-02 08:00:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68095,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Visualization of the coffee fermentor (b) sketch of the coffee fermentor.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8983516/v1/bf5c1e08b3e8d299b64f664d.jpg"},{"id":103724552,"identity":"c7b387a3-7c87-467c-81ec-b32e99d48fbf","added_by":"auto","created_at":"2026-03-02 08:00:40","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":78342,"visible":true,"origin":"","legend":"\u003cp\u003eSensory diagram of Bondowoso Arabica coffee from Fermentation.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8983516/v1/5330901d707b1e9f9fdfac1f.jpg"},{"id":104400166,"identity":"278722eb-18ab-4f05-aab0-6847c954a996","added_by":"auto","created_at":"2026-03-11 12:09:05","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":104393,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Gasproduction during fermentation (b) COD, BOD, and TSS in anaerobic (c) contribution of anaerobic.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8983516/v1/a5c369c8c0573fff759a0309.jpg"},{"id":104410568,"identity":"419c1233-48f5-498b-87a4-ad718821216d","added_by":"auto","created_at":"2026-03-11 12:52:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1275574,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8983516/v1/e2d6240e-f154-41ae-ac28-f2a1fed38084.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Life Cycle Assessment – Evaluating the Physical and Sensory Quality of Bondowoso Arabica Coffee through Precision Fermentation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCoffee production underpins rural livelihoods across many tropical countries and remains especially critical for smallholders, who contribute a substantial proportion of global supply and rely on coffee sales as a primary source of household income. In Indonesia, coffee is an export-oriented strategic commodity, and current development efforts increasingly emphasize quality improvement at both the farm and post-harvest stages to enable access to higher-value specialty markets. Indonesia cultivates both Arabica (\u003cem\u003eCoffea arabica\u003c/em\u003e) and Robusta (\u003cem\u003eCoffea canephora\u003c/em\u003e). While Robusta typically dominates national production in volume, Arabica contributes a smaller share yet provides higher economic value because it is closely associated with specialty-grade characteristics and origin-based differentiation. Arabica is generally grown at higher elevations (commonly\u0026thinsp;\u0026ge;\u0026thinsp;700 m a.s.l.), whereas Robusta is more tolerant of lower elevations and warmer conditions, resulting in distinct production landscapes, harvesting calendars, and post-harvest processing choices.\u003c/p\u003e \u003cp\u003eThe commercialization of specialty coffee is closely linked to standardized sensory evaluation and market systems that reward consistent cup quality. The Specialty Coffee Association (SCA) uses a 100-point cupping framework in which coffees scoring\u0026thinsp;\u0026ge;\u0026thinsp;80 points are commonly classified as \u0026ldquo;specialty.\u0026rdquo; High-performing lots may further enter premium competitions and auctions such as the Cup of Excellence (COE), which applies multi-stage sensory screening and recognizes top-scoring coffees with substantial market premiums. In practice, participation in COE-type mechanisms can motivate farmers and processors to implement stricter cherry selection, improved defect control, and better traceability, thereby discouraging quality mixing and supporting a shift from commodity-grade coffee toward differentiated specialty lots. This pressure to meet international benchmarks is intensified by the global scale of coffee production; FAO records indicate that coffee is produced in more than 80 producing countries and territories, highlighting the strong competition in quality-based export markets.\u003c/p\u003e \u003cp\u003ePost-harvest processing is a major determinant of final cup quality because it regulates the chemical and biochemical transformations that occur between harvest and drying. Broadly, coffee is processed using wet (washed) or dry (natural) methods, and both approaches can produce high-quality products depending on cultivar, ripeness, processing hygiene, and environmental conditions. Fermentation\u0026mdash;whether spontaneous or controlled\u0026mdash;is particularly influential because microorganisms modify sugars, acids, and aroma precursors in the mucilage and bean surface environment, which later shape the sensory attributes after roasting. Recent studies demonstrate that manipulating fermentation duration (including extended fermentation up to ~\u0026thinsp;96 h), oxygen exposure (anaerobic or self-induced anaerobiosis), and the use of inoculation/starter cultures can generate distinct chemical profiles and sensory outcomes. Consequently, \u0026ldquo;precision fermentation\u0026rdquo; has emerged as a promising concept in specialty processing, aiming to improve reproducibility by controlling key parameters such as time, temperature, and mass transfer.\u003c/p\u003e \u003cp\u003eAnaerobic fermentation performed in a controlled fermentor is especially important because it converts fermentation from a highly variable, artisanal step into a standardized and repeatable unit operation. Conventional anaerobic-style processing (e.g., sealed bags or drums under ambient conditions) often experiences uncontrolled temperature changes, uneven contact between mucilage and microbial communities, inconsistent oxygen exclusion, and variable contamination from the surrounding environment. These factors influence microbial succession and metabolic pathways, thereby affecting the formation of organic acids, alcohols, and aroma-active intermediates. As a result, sensory outcomes may vary markedly between batches, and the risk of defects\u0026mdash;such as over-fermentation, unbalanced acidity, harsh/solvent-like notes, or undesirable \u0026ldquo;ferment\u0026rdquo; characteristics\u0026mdash;may increase. Importantly, evidence from fermentation research indicates that relatively small deviations in fermentation time and temperature can meaningfully shift volatile composition and aroma profiles, reinforcing the need for tight operational control when targeting specialty-grade consistency.\u003c/p\u003e \u003cp\u003eA fermentor-based approach enables control of three parameters that are particularly decisive in anaerobic coffee processing: (i) oxygen management, (ii) temperature regulation, and (iii) mixing/agitation. First, reliable oxygen limitation stabilizes anaerobic conditions and reduces uncontrolled microbial contamination, improving the predictability of fermentation trajectories. Second, temperature control is essential because fermentation kinetics are temperature dependent; without regulation, accelerated reactions can drive rapid acidification and excessive metabolite accumulation, increasing the likelihood of defects. Third, controlled mixing improves mass transfer and batch uniformity by reducing local gradients (e.g., hotspots, stagnant zones, uneven mucilage breakdown), which helps ensure consistent biochemical conversion across the entire batch. In addition to quality benefits, a closed and controlled fermentor strengthens environmental assessment because it enables systematic monitoring of process outputs, including gas production, and facilitates a more reliable mass-balance for life cycle inventory development.\u003c/p\u003e \u003cp\u003eMoreover, improving coffee quality must be evaluated alongside environmental performance. Coffee processing can generate high-strength wastewater and emissions, and unmanaged by-products may contribute to local pollution as well as climate impacts. Coffee effluents often contain high organic loads (reflected in elevated COD and BOD) and suspended solids, which can degrade receiving water bodies if discharged without adequate treatment. From a climate perspective, energy use, wastewater handling (including potential methane generation under anaerobic conditions), and by-product management can significantly influence Global Warming Potential (GWP) outcomes in Life Cycle Assessment (LCA). Anaerobic streams may also provide mitigation opportunities if captured and valorized (e.g., through biogas recovery); however, this requires measurement-based inventory data, transparent assumptions, and appropriate system boundaries.\u003c/p\u003e \u003cp\u003eIn this study, Arabica coffee was selected because it is more strongly associated with the specialty segment and typically commands higher price premiums than Robusta when quality attributes (floral/fruity notes, acidity balance, and aroma complexity) are achieved consistently. Arabica is also generally more sensitive to post-harvest processing conditions, meaning that variations in fermentation temperature, duration, oxygen exposure, and mixing can lead to pronounced changes in organic acid formation, volatile precursor development, and ultimately cup profile. This sensitivity is advantageous for process research because it allows the effect of controlled fermentation to be detected more clearly through cup testing and quality grading. In addition, Arabica in Indonesia is commonly cultivated at higher altitudes, where cooler conditions and slower cherry maturation can support higher sensory potential, but this potential can be lost if fermentation is uncontrolled or inconsistent. Therefore, using Arabica provides a relevant and high-impact case to test whether a controlled fermentor can deliver repeatable specialty-quality improvements while also enabling more reliable measurement of emissions and wastewater characteristics for LCA and GWP accounting.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe study was carried out with the Bondowoso Coffee Group in Pakis Village, Bondowoso Regency, East Java Province, Indonesia. The plantations were located at 1,048\u0026ndash;1,400 m above sea level (latitude\u0026thinsp;\u0026minus;\u0026thinsp;7.9231361, longitude 113.708889), representing a typical upland Arabica production zone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Raw material selection\u003c/h2\u003e \u003cp\u003eFully ripe red cherries were collected during the Arabica harvest season (May\u0026ndash;June 2025) from plantations managed by the Bondowoso Coffee Group in the Argopuro Mountain region. The coffee trees in the study area have been cultivated for approximately 33 years. Cherries were manually sorted to remove visible defects (e.g., insect damage, mold, broken skin, and underripe/overripe fruit). The soluble solids content of cherry juice ranged from 11% to 17% \u0026deg;Brix, measured using an automatic temperature-compensated (ATC) optical refractometer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Processing design and treatments\u003c/h2\u003e \u003cp\u003ePost-harvest processing was designed as controlled anaerobic fermentation in a closed fermentor followed by washing and drying. Freshly harvested ripe cherries were pulped to obtain coffee beans with mucilage (CB). The pulped CB were transferred into the fermentor and fermented for 96 h under anaerobic conditions. A controlled water addition was applied to support uniform fermentation and facilitate mucilage breakdown prior to subsequent washing and drying. Three experimental groups were evaluated: (i) control (no fermentation), (ii) anaerobic fermentation at 30\u0026deg;C, and (iii) anaerobic fermentation at 35\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Fermentor system\u003c/h2\u003e \u003cp\u003eA 5 kg per batch fermentation system was developed through collaborative research between the University of Jember and PT SAS (Jember), supported by BRIN funding [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The fermentor was equipped with an automatic stirrer (\u0026asymp;\u0026thinsp;25 rpm), a water-jacket heater for temperature regulation (30\u0026ndash;35\u0026deg;C) [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and a radiator-based cooling unit to prevent overheating when jacket temperature exceeded\u0026thinsp;~\u0026thinsp;36\u0026ndash;40\u0026deg;C. Fermentation pH was monitored as an indicator of process progression [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Electricity demand was measured and reported as kWh per hour and per batch (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Physical quality assessment (SNI)\u003c/h2\u003e \u003cp\u003eGreen bean (GB) grade was determined using the Indonesian National Standard (SNI) defect-point system [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Approximately 300 g of beans were sampled after sorting. Defect values were summed to obtain a total defect score, which was then used to classify the coffee into Grades 1\u0026ndash;6 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eDefect parameter score SNI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade Quality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefect Parameters Score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81\u0026ndash;150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151\u0026ndash;225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Sensory evaluation (SCA/CQI)\u003c/h2\u003e \u003cp\u003eSensory evaluation was performed at the Indonesian Coffee and Cocoa Research Institute (ICCRI) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] following the SCA cupping protocol and evaluated by at least two certified Q Arabica Graders [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. All samples were roasted to medium roast (Agtron 55) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Attribute scores and total score were recorded, including any taint/defect notes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Gas and wastewater measurements\u003c/h2\u003e \u003cp\u003eFermentation headspace gas was captured and analyzed by gas chromatography (GC) at the University of Jember to determine CO₂ and CH₄ concentrations. While liquid effluent from fermentation process was measured chemical oxygen demand (COD), biochemical oxygen demand (BOD), and total suspended solid (TSS) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Life cycle assessment\u003c/h2\u003e \u003cp\u003eThe functional unit was 1 kg of coffee powder (CP). Primary data from observations and experiments were combined with secondary background data in SimaPro v9.3.0.3. Impact assessment was performed using the ReCiPe 2016 Midpoint (H) method [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Electricity consumption was modeled using the local grid supply. Process-stage energy demand is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and the mass balance is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnergy demand at every stage*\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaterials Arabica\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eProcess\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFlow Processing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnaerobic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMotor Cycle (Petrol), litre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWashing Cherry Beans, litre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDepulping, kWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFermentor, kWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDehulling, kWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRoasting Coffee, kWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRoasting Coffee Skin, kWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGrinding Coffee, kWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*Data were measured.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMass balance by processing method for Bondowoso Arabica coffee*\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStages process\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnaerobic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCherry Beans, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepulping, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInject water, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFermentation, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrying CB/Pulp, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDehulling, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHand Sorting, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoasting GB, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrinding, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e*Data were measured.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Physical processing and green bean defects\u003c/h2\u003e \u003cp\u003eOn-farm and post-harvest handling influence bean integrity and defect formation. Field-related defects can arise from insect damage, notably the coffee berry borer (Hypothenemus hampei), which can reduce bean density and promote spoilage [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Processing and storage can also introduce defects such as cracked beans, parchment fragments, and discoloration associated with improper moisture control. Based on SNI grading, the final defect score for the anaerobic sample was 13.5, corresponding to Grade 2 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Maintaining Grade 2 (defect score 12\u0026ndash;25) reduces the likelihood of taint/defect attributes during cupping and supports specialty market targeting.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDefect score analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType of Defect (Seed)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eDefect score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eQS An\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eSV An\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Black seed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Half seed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Cracked black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Dried cherry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Dark brown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 HS big-sized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 HS medium-sized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 HS small-sized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Parch skin seed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Parch big-sized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Parch medium-sized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Parch small-sized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Cracked seed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Pale seed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Hole seed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Hole (more than 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Freckles seed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Branch big-sized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Branch medium-sized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Branch small sized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFinal defect score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e13.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eQuantity seeds\u0026thinsp;=\u0026thinsp;QS; Score value\u0026thinsp;=\u0026thinsp;SV; Anaerobic\u0026thinsp;=\u0026thinsp;An\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Sensory quality\u003c/h2\u003e \u003cp\u003eCupping results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, with descriptive notes in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The 35\u0026deg;C treatment achieved the highest total score (84.50), with improved balance and sweetness-associated descriptors relative to the control and 30\u0026deg;C treatment. These results align with reports that controlled anaerobic fermentation and inoculation can yield fruit-forward profiles and specialty-level scores [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Because SCA commonly classifies scores of 80\u0026ndash;84.99 as \u0026lsquo;Very Good\u0026rsquo; and \u0026ge;\u0026thinsp;85 as \u0026lsquo;Excellent\u0026rsquo;, the coffees in this study fall within the Very Good\u0026ndash;Specialty range [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensory quality attributes for Bondowoso Arabica coffee*\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCup test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtributes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003csup\u003e0\u003c/sup\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003csup\u003e0\u003c/sup\u003eC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFragrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlavor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAftertaste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniformity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBalance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClean Cup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweetness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e82.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e82.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e84.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Data were measured from the panelist ICCRI\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal score, classification, and description of Bondowoso Arabica\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcessing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNotes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcellent-Specialty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eToasty, nutty, soybean aroma, grassy, spicy-chili, brown sugar\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003csup\u003e0\u003c/sup\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcellent-Specialty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNutty, soybean aroma, grassy, potato\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003csup\u003e0\u003c/sup\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcellent-Specialty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrown sugar, nutty, honey, spicy-chili-honeyed-tea rose, black tea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Data were measured from panelist ICCRI\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Emissions and wastewater characteristics\u003c/h2\u003e \u003cp\u003eGas production profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) showed peak CO₂ on day 2 (\u0026asymp;\u0026thinsp;34,550 ppm) followed by a decline by day 4 (\u0026asymp;\u0026thinsp;11,340 ppm). CH₄ was detected at lower concentrations (\u0026asymp;\u0026thinsp;1.96 ppm on day 1 to \u0026asymp;\u0026thinsp;3.2 ppm on day 3), but was included due to its higher climate impact per unit mass. Wastewater COD, BOD, and TSS (Fig.\u0026nbsp;6) were measured and included in the life cycle inventory to represent effluent burden [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 GWP results and hotspot analysis\u003c/h2\u003e \u003cp\u003eThe modeled GWP from CB to CP was 1.39 kg CO₂-eq per 1 kg CP (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Electricity consumption for fermentor operation (2.13 kWh per batch) was a major contributor, accounting for 0.67 kg CO₂-eq of the total, while the remaining stages contributed 0.72 kg CO₂-eq.\u0026nbsp;These results indicate a quality\u0026ndash;environment trade-off: controlled fermentation improves reproducibility and sensory performance but can increase GWP via electricity demand. Optimizing insulation, heating efficiency, batch scheduling, and using lower-carbon electricity could reduce this hotspot.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of this study on the anaerobic process.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCoffee processing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eStages\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCB to GB\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCB to CP\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnaerobic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9 kg CO\u003csub\u003e2\u0026minus;eq\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39 kg CO\u003csub\u003e2\u0026minus;eq\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAA and EH: Writing \u0026ndash; original draft, Conceptualization, and Methodology. NN, J, and JK: Formal analysis.NA and TL: Data curation. YW and NP: Project administration.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank the Director of Research and Community Service (DPPM). This research was supported by Impactful Leading Consortium Research Funding Program (grant numbers 0923/C3/DT.05.00/2025). We also thank the University of Jember for the convenience of their research facilities\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eR. N. Valenzuela Antezana and G. I. 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Bobkov\u0026aacute; \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Comparative Analysis of Selected Chemical Parameters of Coffea arabica, from Cascara to Silverskin,\u0026rdquo; \u003cem\u003eFoods\u003c/em\u003e, vol. 11, no. 8, 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/foods11081082\u003c/span\u003e\u003cspan address=\"10.3390/foods11081082\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8983516/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8983516/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Coffee production is vital for the economic activities of smallholders for annual income, Arabica Coffee Bondowoso, Indonesia. Smallholder coffee farms can increase prices and break the long sales chain with quality specialty coffee. This study aims to understand in-depth precision fermentation as an evaluation of the physical and sensory quality of Green Beans (GB). A Life Cycle Assessment (LCA) was applied to evaluate the Global Warming Potential (GWP). Observation and experimentation were chosen for the coffee process: anaerobic. Primary data were combined with secondary data calculations using the ReciPe 2016 method, Simapro v9.3.03. The results of this study provide coffee category grade two, including coffee scores of 84.25, respectively, for anaerobic. GWP from processed Cherry Beans (CB) to Coffee Powder (CP) are 1.39 kg CO2-eq for one kg CP, respectively.\nKeywords: Life cycle assessment (LCA), Global warming potential (GWP), anaerobic, precision fermentation, sensory quality.","manuscriptTitle":"Life Cycle Assessment – Evaluating the Physical and Sensory Quality of Bondowoso Arabica Coffee through Precision Fermentation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 08:00:34","doi":"10.21203/rs.3.rs-8983516/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d59639fc-b154-4043-bfea-7add1fc01a19","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63625796,"name":"Earth and environmental sciences/Environmental sciences"},{"id":63625797,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-03-02T08:00:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 08:00:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8983516","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8983516","identity":"rs-8983516","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}