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Coffee post-processing drying eliminates moisture content, reduces fungal and microbe growth, and develops unique aroma and flavor compounds. Thai coffee producers use controlled-environment drying (CED) techniques to improve the quality and cupping scores of the coffee. This research investigated how different drying methods, including sun drying (SD), controlled-environment drying at 20–30 °C, 50–55% RH, and fast drying (FD) at 30–45 °C influenced the physicochemical characteristics of coffee undergoing dry (DP), washed (WP), and honey (HP) processing. Results showed that true density, moisture content, water activity, color, caffeine, trigonelline, chlorogenic acid, caffeic acid, sucrose, and fructose in green coffee beans were significantly affected (p < 0.05) by both drying technique and post-harvest processing. Drying techniques and processing directly impacted the characteristics of green (GCB) and roasted coffee beans (RCB). Findings suggested a correlation between CED, SD, and FD based on the physicochemical and biochemical properties and sugar contents of both green and roasted coffee beans. [ABSTRACT FROM AUTHOR]

Coffee bean oxidation is associated with enzymatic and non-enzymatic browning, the degradation of desirable aromatic compounds, the development of undesirable flavors, increased susceptibility to microbial spoilage, and volatile compound losses. This study investigated natural dry process (DP) and honey process (HP) green coffee beans stored in GrainPro® bags for 0, 5, 10, and 20 days under accelerated storage conditions at 30 °C, 40 °C, and 50 °C with relative humidity of 50%. A kinetic model was used to estimate the shelf life of the green coffee beans. DP recorded durability of 45.67, 29.9, and 24.92 days at 30 °C, 40 °C, and 50 °C, respectively, with HP 60.34, 38.07, and 19.22 days. Partial least squares (PLS) analysis was performed to build the models in order to predict the shelf life of coffee based on peroxide (PV) and thiobarbituric acid reactive substances (TBARS) values. In terms of prediction with leave-one-out cross-validation (LOOCV), PLS provided a higher accuracy for TBARS ( R 2 = 0.801), while PV was lower ( R 2 = 0.469). However, the auto-prediction showed good agreement among the observed and predicted values in both PV ( R 2 = 0.802) and TBARS ( R 2 = 0.932). Based on the variable importance of projection (VIP) scores, the ATR-FTIR peaks as 3000–2825, 2154–2150, 1780–1712, 1487–2483, 1186–1126, 1107–1097, and 1012–949 cm−1 were identified to be the most related to PV and TBARS on green coffee beans shelf life. ATR-FITR showed potential as a fast and accurate technique to evaluate the oxidation reaction that related to the loss of coffee quality during storage. [ABSTRACT FROM AUTHOR]

Chilli was dried using conventional tray dryer with 3 different drying conditions, i.e., 60°C, 80°C and 2-stage drying (80°C and 60°C). The effects of pre-treatment methods were evaluated by soaking chilli in pre-treatment solutions prior to drying. In terms of drying characteristics, the drying time found to be shortest at 80°C. It was also found that the drying rate of pre-treated chilli increased compared with non-treated one. The modified Page’s model was suitable for describing drying characteristics at all conditions (R2 = 0.97-0.99). Furthermore, the color change during drying was investigated using image analysis methods. It was found that the pre-treatment methods prevented the color change. The combined kinetic model was the best model for predicting the change of color during drying (R2 = 0.98-0.99). A pre-treated chilli soaking in a mixed solution of 0.3% (w/w) Na2S2O5 and 1% (w/w) CaCl2 showed the better quality. In addition, the combined kinetics model could describe the change of L* a* and b* values in all conditions.

Cavendish banana is an important export product of many countries, while postharvest banana classification also impacts plantation income. The quality inspection standard classifies Cavendish banana into three groups based on fruit size as large, medium and small. Most factories classify bananas manually which is time-consuming and also prone to errors. To expedite and enhance this process, a new fast and reliable method is proposed for classifying Cavendish banana gradings. The dataset contained 415 records, which were divided into five classes: L1, L2, L3, L4, and Reject. This study proposes employing a Synthetic Minority Oversampling Technique (SMOTE) to address imbalanced data by synthesizing the minority class to generate new minority sample data. Then a Random Forest classifier based on fruit length and fruit diameter parameters is implemented to efficiently classify Cavendish banana grading. Performance of the proposed method was assessed using 10-fold cross-validation. Compared to other machine learning techniques, results revealed that our approach delivered excellent performance with highest classification accuracy of 97.88%. Accuracy of 95.38% was achieved for an unseen testing dataset, illustrating superior performance to the other methods. We finally demonstrate a web application of the Cavendish banana grade classification system that was developed from the proposed model. [ABSTRACT FROM AUTHOR]