Mango peel, a byproduct from the mango processing industry, is a potential source of food-grade mango peel pectin (MPP). Nonetheless, the influence of fruit physical characteristics and phytochemicals of peels on their correspondent pectin level has never been examined, particularly when high-quality food additives are of commercial need. Subsequently, the ultimate aim of the present study was to comprehend their relationship using chemometric data analyses as part of raw material sourcing criteria. Principal component analysis (PCA) advised that mangoes of ‘mahachanok’ and ‘nam dok mai’ could be distinguished from ‘chok anan’ and ‘kaew’ on the basis of physiology, peel morphology, and phytochemical characteristics. Only pectin extracted from mango var. ‘chok anan’ was classified as low-methoxyl type (Mox value ~4%). Using the partial least-squares (PLS) regression, the multivariate correlation between the fruit and peel properties and the degree of esterification (DE) value was reported at R² > 0.9 and Q² > 0.8. The coefficient factors illustrated that yields of byproducts such as seed and total biomass negatively influenced DE values, while they were positively correlated with crude fiber and xylose contents of the peels. Overall, it is interesting to highlight that, regardless of the differences in fruit varieties, the amount of biomass and peel proximate properties can be proficiently applied to establish classification of desirable properties of the industrial MPP.

The potential use of colorimetric sensors has received significant attention due to its feasibility for use in various applications. After reacting with a sample, the image of the colorimetric sensor can be captured and converted into digital data using several different color models. The analytical data can then be processed with various chemometric methods. This research study investigated the predictive performance of calibration models established using color models commonly used in analytical chemistry including RGB, CMYK, HSV and CIELAB. A total of eight commercially available colorimetric sensors were used to determine the presence of manganese (Mn²⁺), copper (Cu²⁺), iron (Fe²⁺/Fe³⁺), nitrate (NO₃^–), phosphate (PO₄^3–), sulfate (SO₄^2–), as well as total hardness and pH values. As external validation tests, real water samples collected in Chiang Mai, Thailand were used. Based on the resulting data obtained using the synthetic test samples, the color that was most similar to the appearing color of the chemical sensor could offer satisfactory results. However, it was not always the case especially when the strips composed of multiple colorimetric sensors or sensor array were used. When tested with external validation, the predictive performance could be improved using appropriate data preprocessing and, in this research study, a normalization method was recommended to guarantee the accuracy of the calibration models.

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.