Mango ‘Nam dok mai si thong’ is in high demand worldwide, displaying desirable attributes which include a particularly sweet flavour and visually appealing appearance. Physiological and biochemical changes that occur in the fruit post-harvest are key factors in determining fruit quality and, consequently, predicted shelf life. In order to understand which post-harvest markers play crucial roles during the ripening process, as well as those which are a consequence of disease infection and physical damage caused by essential oil vapour, partial least squares (PLS) correlation models were used. During storage, physiochemical (percent weight loss, peel colour, firmness, pH, and peel electrolyte leakage) and biochemical (titratable acidity, total soluble solids, total phenolic compounds, total flavonoid compounds, antioxidants, total sugar, and reducing sugar content) parameters, as well as near-infrared (NIR) spectra, were monitored and correlated with visual post-harvest physiological responses. The majority of analysed markers displayed distinct relationships with the ripening process of mangoes, where for non-destructive parameters (R² = 0.86), lightness (L*) and b* value were notably significant, and for destructive parameters (R² = 0.79), pH and total soluble solids were notably significant. Similarly, the same markers were also correlated with physical damage and post-harvest mango disease infection severity, possibly through polysaccharide deformation and activation of browning-related enzymes. NIR imaging results also revealed the absorbent regions involved in biochemical alterations (water and enzymes; absorbance at 1170 nm, 1400–1500 nm, and 2150–2250 nm) that pertain to the fruit’s quality. The findings from this work provide an initial step towards the development and assessment of quality measures for ‘Nam dok mai si thong’ mango.

This research aimed to improve the classification performance of a developed near-infrared (NIR) spectrometer when applied to the geographical origin identification of coffee bean samples. The modification was based on the utilization of a collection of spectral databases from several different agricultural samples, including corn, red beans, mung beans, black beans, soybeans, green and roasted coffee, adzuki beans, and paddy and white rice. These databases were established using a reference NIR instrument and the piecewise direct standardization (PDS) calibration transfer method. To evaluate the suitability of the transfer samples, the Davies–Bouldin index (DBI) was calculated. The outcomes that resulted in low DBI values were likely to produce better classification rates. The classification of coffee origins was based on the use of a supervised self-organizing map (SSOM). Without the spectral modification, SSOM classification using the developed NIR instrument resulted in predictive ability (% PA), model stability (% MS), and correctly classified instances (% CC) values of 61%, 58%, and 64%, respectively. After the transformation process was completed with the corn, red bean, mung bean, white rice, and green coffee NIR spectral data, the predictive performance of the SSOM models was found to have improved (67–79% CC). The best classification performance was observed with the use of corn, producing improved % PA, % MS, and % CC values at 71%, 67%, and 79%, respectively.