This research investigated the impact of the concentration of pineapple juice on the characteristics of pineapple wine during fermentation with Saccharomyces cerevisiae var. burgundy. Three ratios of fresh pineapple juice to water were mixed to obtain three treatments, which were T1—2:1, T2—1:1, and T3—1:2. The °Brix and pH of all pineapple juice and water ratios were adjusted to 25 and 4, respectively. The results showed that changes in alcohol, pH, Total Soluble Solids (TSS), Total Titratable Acidity (TAA, as citric acid), and Volatile Acidity (VA, as acetic acid) during the 10-day fermentation among three treatments were significantly different. The highest alcohol content was obtained from the 2:1 with values of 10.71% (v/v). The mixed ratio at 1:1 and 1:2 obtained the alcohol value of 9.61 and 8.35% (v/v), respectively. After ten days of fermentation, TSS, pH values, TAA, and VA were in the range of 9.7–13 °Brix, 3.56–3.82, 0.384–0.448, and 0.0013–0.0016, respectively. However, the appearance, aroma, and taste of all ratios were not significantly different. Sweetness and overall liking, wine with pineapple juice/water ratio at 2:1 had the highest score (p ≤ 0.05). The total antioxidant activities determined by DPPH and total phenolic content were 0.91 mmol/L TE and 365.80 mg/L GAE, respectively, as confirmed by FTIR spectral analyses.

This study used Fourier transform-near-infrared (FT-NIR) spectroscopy equipped with the liquid probe in combination with an efficient wavelength selection method named searching combination moving window partial least squares (SCMWPLS) for the determination of ethanol, total soluble solids, total acidity, and total volatile acid contents in pineapple fruit wine fermentation using Saccharomyces cerevisiae var. burgundy. Two fermentation batches were produced, and the NIR spectral data of the calibration samples in the wavenumber range of 11,536–3952 cm⁻¹ were obtained over ten days of the fermentation period. SCMWPLS coupled with second derivatives searched and optimized spectral intervals containing useful information for building calibration models of four parameters. All models were validated by test samples obtained from an independent fermentation batch. The SCMWPLS models showed better predictions (the lowest value of prediction error and the highest value of residual predictive deviation) with acceptable statistical results (under confidence limits) among the results achieved by using the whole region. The results of this study demonstrated that FT-NIR spectroscopy using a liquid probe coupled with SCMWPLS could select the optimized wavelength regions while reducing spectral points and increasing accuracy for simultaneously monitoring the evolution of four chemical parameters in pineapple fruit wine fermentation.

Pineapples are a tropical fruit with high nutritional value and high vitamin and sugar contents. In this study, low-grade pineapples were fermented to produce vinegar using surface culture fermentation (SCF), which involved the addition of dragon fruit juice, to compare the quality and antioxidant activity of different preparations of vinegar. The highest acetic acid concentration (7.35%) was obtained from pineapple vinegar after 20 days of incubation. Vinegar made from mixed pineapple and dragon fruit juice without peel and vinegar with pineapple and dragon fruit juice with peel had acetic acid concentrations of up to 6.20% and 4.50%, respectively. The mixed-fruit vinegar of pineapple and dragon fruit juice with peel displayed the highest antioxidant activity at 210.74 µg/g TE, while no significant difference was found between the other two vinegars (189.52 vs. 187.91 µg/L TE). Notably, the volatile compounds detected in the vinegars were alcohols and esters, which may contribute to the distinct aroma. Overall, the addition of dragon fruit juice with peel to pineapple vinegar increased the phenolic content and antioxidant activity; however, fermentation was slightly slower than that of the other two test materials.

In this study, the performance of a near-infrared (NIR) fiber-optic probe coupled with stability competitive adaptive reweighted sampling (SCARS) was investigated for the analysis of acetic acid, ethanol, total soluble solids, caffeic acid, gallic acid, and tannic acid in the broth of pineapple vinegar during fermentation. The NIR spectra of the broth samples in the region of 11,536–3956 cm⁻¹ were collected during vinegar fermentation promoted by Acetobacter aceti. This continuous biological process led to changes in the concentrations of all analytes studied. SCARS provided optimized and stabilized NIR spectral variables for the construction of a partial least squares (PLS) model for each analyte using a small number of optimal variables (under 88 variables). The SCARS-PLS model outperformed the conventional PLS model, and achieved excellent accuracy in accordance with ISO 12099:2017 for the four prediction models of acetic acid, ethanol, caffeic acid, and gallic acid, with root-mean-square error of prediction values of 0.137%, 0.178%, 0.637 μg/mL and 0.640 μg/mL, respectively. In contrast, only an acetic acid content prediction model constructed via the conventional PLS method and using the whole spectral region (949 variables) could pass with acceptable accuracy. These results indicate that the NIR optical probe coupled with SCARS is an appropriate method for the continuous monitoring of multianalytes during vinegar fermentation, particularly acetic acid and ethanol contents, which are indicators of the finished fermentation of pineapple vinegar.

This study aimed to develop an effective method for detecting wheat flour contamination in rice flour using near-infrared hyperspectral imaging (NIR-HSI), with the goal of supporting raw material quality control and food safety. Rice flour samples contaminated with varying levels (0%–100%) of low-gluten and high-gluten wheat flour were analyzed using NIR-HSI, and classification models were developed using partial least squares–discriminant analysis (PLS-DA). The developed models successfully discriminated between pure rice flour and wheat flour–contaminated rice flour. The selected models based on the original spectra achieved classification accuracies of 98.18% and 100.00% for low-gluten and high-gluten wheat flour, respectively, in the independent prediction set. For the low-gluten wheat flour model, sensitivity, specificity, and F1-score values were 97.78%, 100.00%, and 98.88%, respectively, while all classification metrics for the high-gluten wheat flour model were 100.00%. The original spectra provided comparable or better classification performance than the SNV- and second derivative–pretreated spectra. In addition, color maps generated from the PLS-DA models enabled visualization of the spatial distribution of wheat flour contamination across the investigated concentration range. These findings demonstrate the potential of NIR-HSI combined with PLS-DA as a rapid and non-destructive approach for detecting and visualizing wheat flour contamination in rice flour.

Authentication of edible bird’s nest (EBN) in heterogeneous matrices is challenging because whole-region spectral averaging may obscure localized variation. This study developed a block-based near-infrared hyperspectral imaging (NIR-HSI) framework integrating complementary classifiers and spatial multi-model agreement mapping. The development dataset comprised 90 independently prepared physical samples, each with four independently sampled and repacked scans; external validation used 21 additional physical samples (84 scans). Spectra (900–1700 nm) were represented by mean-spectrum or block-based approaches and classified using partial least squares discriminant analysis (PLS-DA), random forest (RF), and Multiple Instance Adaptive Cosine Estimator (MI-ACE). Model development used leave-one-physical-sample-out cross-validation. In external validation, mean-spectrum PLS-DA achieved accuracies of 0.7143–0.8571, whereas mean-spectrum RF correctly classified all 21 samples. For 9 × 9 blocks, PLS-DA achieved accuracies of 1.0000, 0.9048, and 1.0000 across the three preprocessing conditions, while RF and MI-ACE correctly classified all 21 samples under all conditions. All-ROI analysis identified a significant second-derivative difference at approximately 1505.65 nm between EBN-positive and EBN-free classes (U = 0, p < 0.001). This region is potentially informative but should not be considered EBN-specific. Overall, the framework provided proof-of-concept evidence of strong physical-sample-level classification while preserving spatial information.