Volume 19, Issue 11

Short-wave infrared radiation (SIR) was used to improve the quality of peanut oil (PO) and peanut meal protein (PMI) in this study. The results showed that SIR roasting was beneficial to increase the yield and improve the quality and flavor of PO. The oxidative stability index (OSI) was improved, the contents of tocopherols and polyphenols increased and the contents of benzo (a) pyrene and polar substances decreased. Besides, the characteristic flavor substances were detected but not for the burnt smell. The effects of SIR on the structural and functional properties of PMI were investigated. It was found that SIR roasting could induce partial denaturation of PMI, thus improving its functional properties such as solubility, emulsifying and foaming, but did not involve the change of secondary structure. It was confirmed that SIR is a potential and available pre-roasting method to improve the comprehensive utilization of peanut resources.

In this study, the effects of Danisco mixed lactic acid bacteria and single Bifidobacterium lactis HCS04-001 fermentation on metabolites in soy yogurt were investigated. The main volatile metabolites in sample D fermented by Danisco mixed lactic acid bacteria were aldehydes, followed by alcohols and ketones. In contrast, the main flavor substances in sample SQ fermented by single Bifidobacterium lactis HCS04-001 were alcohols followed by aldehydes. L-Arginine, S-Adenosylhomocysteine, Glutamyltyrosine, Gly-Trp, Ser-Trp, Ile-Ser-Leu, Pro-Val-Leu etc. and D-Ornithine, Citrulline, Glutamylproline, organic acid were the main non-volatile differential metabolites that distinguished the soy yogurt samples. Arginine biosynthesis and glutathione metabolism were important pathways leading to differences in metabolic profiles. This study provides detailed insight into the metabolic properties of commercial Danisco mixed lactic acid bacteria and single Bifidobacterium lactis HCS04-001 fermentations, which will contribute to the development of soy yogurt with good nutritional and sensory qualities.

Crops’ health is affected by a varied range of diseases. Convenient and precise diagnosis plays a substantial role in preventing the loss of crop quality. In the past decade, deep learning (DL), particularly Convolutional Neural Networks (CNNs), has presented extraordinary performance for diverse applications involving crop disease (CD) detection. In this study, a comparison is drawn for the three pre-trained state-of-art architectures, namely, EfficientNet B0, ResNet50, and VGG19. An ensembled CNN has also been generated from the mentioned CNNs, and its performance has been evaluated over the original coloured, grey-scale, and segmented dataset. K-means clustering has been applied with six clusters to generate the segmented dataset. The dataset is categorized into three classes (two diseased and one healthy class) of bean crop leaves images. The model performance has been assessed by employing statistical analysis relying on the accuracy, recall, F1-score, precision, and confusion matrix. The results have shown that the performance of ensembled CNNs’ has been better than the individual pre-trained DL models. The ensembling of CNNs gave an F1-score of 0.95, 0.93, and 0.97 for coloured, grey-scale, and segmented datasets, respectively. The predicted classification accuracy is measured as: 0.946, 0.938, and 0.971 for coloured, grey-scale, and segmented datasets, respectively. It is observed that the ensembling of CNNs performed better than the individual pre-trained CNNs.

The herbal tea market is projected to grow at an annual rate of 4.8 %, with the discrimination of these products appearing as an issue of food quality and safety. In this study the Vis/NIR spectroscopy combined with chemometrics was applied for discriminating five popular herbal teas (chamomile, boldo, lemon grass, carqueja, fennel) by using powdered samples. Dynamic sampling was applied for measuring the spectral signatures and different spectral pre-treatments were evaluated aiming at improving the discrimination accuracy. The Partial Least Squares Discriminant Analysis (PLS-DA) achieved high prediction accuracies (77.8–100 %), specificities (89.4–100 %) and sensitivities (66.1–100 %), with detrending and object-wise standardization pre-treatments correctly discriminating 100 % of the samples during the external validation. The Vis/NIR spectroscopy combined with chemometric analysis has great potential to discriminate powdered herbal teas, providing a non-destructive, fast, safe and chemical-free solution for automated quality control procedures in industries of tea processing.

The current study was designed to predict the response of Aspergillus parasiticus and AFB 1 production as a function of temperature (25, 30, 35, 40 °C), water activity ( a w = 0.57, 0.90, 0.94, 0.96) and growth medium in peanuts and walnuts. The fungal growth, counted as infected nut kernels and AFB 1 content was determined using HPLC. About 100 % kernels of peanut and walnut were infected with A. parasiticus at 30 °C with 0.96 a w . The maximum toxin was quantified at optimal 25 °C × 0.96 a w (4780 μg/kg) in walnuts and 30 °C × 0.96 a w (9100 μg/kg) in peanuts. Whereas, the temperatures (<20 °C or >40 °C) and a w (<0.90) doesn’t provide a sufficient environment for the growth of these entities. Additionally, the sample growth medium was found another major factor that affects toxin production, along with environmental conditions. The regression model and two-way ANOVA indicate that temperature, a w and commodity are the significant predictors ( p < 0.05) for fungal growth and AFB 1 production.

The present study was focused on microwave-roasting (300–600 W for 5–15 min) on the quality attributes of black pepper. The increase in roasting power and time decreased the lightness, L * (51.85–25.64), and a visible color change from medium light to dark was observed. Roasting reduced the powder density (bulk density – 0.51–0.41 g/cm 3 ; tapped density – 0.70–0.53 g/cm 3 ), flowability, and crystallinity and showed a higher Hausner’s ratio (1.29–1.42), carr’s index (22.43–29.38), compressibility index (0.22–0.30), cohesion index (13.96–34.80 g mm/g), and caking strength (5225.79–12040.44 g mm). All the samples showed stable flow, and the stability was in the range of 1–1.11. Sample roasted at lower roasting levels had higher antioxidant properties viz. , phenolic content (36.20–79.62 mg GAE/100 g), flavonoid content (350.52–566.45 mg QE/100 g), % DPPH scavenging activity (53.97–85.12 %), and ABTS assay (2.33–4.81 mmol TE/100 g), whereas the higher roasting conditions reduced the TFC and % DPPH inhibition. The principal component analysis (PCA) and a simple least square method (SLSM) with maximum desirability were used to develop the correlation and optimize the roasting condition, respectively. Results showed that the roasting time had a prominent effect on different quality attributes compared to roasting power, and the optimum roasting condition for BP is 300 W, 15 min.