Effects of different drying methods on sensory qualities and aroma compounds of finger citron (Citrus medica L. var. sarcodactylis Swingle) slices

Radio frequency drying on functional diversity of tiger nut flour: Effects on physicochemical, structural, and rheological properties

Tiger nut (TN) is a valuable nutrient and gluten-free tuber. To achieve high-quality TN flour as functional ingredients in food, it is essential to develop effective drying technologies for TN. Five drying methods including natural drying (Control), hot-air drying (HD), radio frequency single drying (RFSD), RF assisted hot-air drying (RFHD), and RF- vacuum drying (RFVD) were selected and compared to determine their effects on physiochemical, structural, and rheological properties of TN flour. Results showed that RF drying (RFD) significantly improved the hydration, oil-absorbing, and antioxidant activity capacity, especially for RFVD. RFHD exhibited greater color (BI = 13.80 ± 0.05 and C = 10.26 ± 0.05) and reducing sugar content (253.50 ± 2.27 mg d.b.) than RFSD and RFVD. The gelatinization temperature, enthalpy value, and particle size (57.30-269.33 μm) of TN flour were reduced. The structural property results indicated that RFD reduced the relative crystallinity and short-range ordering of the flour, altered protein secondary structure, and caused the damaged microstructure in comparison with Control and HD groups. All sample gels exhibited a weak strain overshoot behavior (type III) under large amplitude oscillations, and RFD resulted in a reduced viscoelastic behavior. RFD could be an effective method to produce functional TN flour.

The grading detection model for fingered citron slices (citrus medica 'fingered') based on YOLOv8-FCS

Introduction

Fingered citron slices possess significant nutritional value and economic advantages as herbal products that are experiencing increasing demand. The grading of fingered citron slices plays a crucial role in the marketing strategy to maximize profits. However, due to the limited adoption of standardization practices and the decentralized structure of producers and distributors, the grading process of fingered citron slices requires substantial manpower and lead to a reduction in profitability. In order to provide authoritative, rapid and accurate grading standards for the market of fingered citron slices, this paper proposes a grading detection model for fingered citron slices based on improved YOLOv8n.

Methods

Firstly, we obtained the raw materials of fingered citron slices from a dealer of Sichuan fingered citron origin in Shimian County, Ya'an City, Sichuan Province, China. Subsequently, high-resolution fingered citron slices images were taken using an experimental bench, and the dataset for grading detection of fingered citron slices was formed after manual screening and labelling. Based on this dataset, we chose YOLOv8n as the base model, and then replaced the YOLOv8n backbone structure with the Fasternet main module to improve the computational efficiency in the feature extraction process. Then we redesigned the PAN-FPN structure used in the original model with BiFPN structure to make full use of the high-resolution features to extend the sensory field of the model while balancing the computation amount and model volume, and finally we get the improved target detection algorithm YOLOv8-FCS.

Results

The findings from the experiments indicated that this approach surpassed the conventional RT-DETR, Faster R-CNN, SSD300 and YOLOv8n models in most evaluation indicators. The experimental results show that the grading accuracy of the YOLOv8-FCS model reaches 98.1%, and the model size is only 6.4 M, and the FPS is 130.3.

Discussion

The results suggest that our model offers both rapid and precise grading for fingered citron slices, holding significant practical value for promoting the advancement of automated grading systems tailored to fingered citron slices.

Optimizing vacuum drying process of polyphenols, flavanols and DPPH radical scavenging assay in pod husk and bean shell cocoa

The objective of this study was to optimize different vacuum drying conditions for cocoa pod husk and cocoa bean shell in order to enhance these by-products for commercial applications. To carry out the optimization, the response surface methodology was applied using a Box-Behnken experimental design with 15 experiments for which different conditions of temperature (X1), drying time (X2) and vacuum pressure (X3) were established. The response variables were the content of total polyphenols, the content of flavanols and the radical scavenging activity evaluated in the extracts of the different experiments. Temperature (50-70 °C), drying time (3-12 h) and vacuum pressure (50-150 mbar) were considered as independent variables. The main factors affecting the response variables were temperature, followed by vacuum pressure. For the content of polyphenols, the optimal response values predicted for the cocoa pod husk was 11.17 mg GAE/g with a confidence limit (95%) of 9.05 to 13.28 mg GAE/g (optimal conditions: 65 °C, 8 h and 75 mbar), while for the cocoa bean shell cocoa was 29.61 mg GAE/g with a confidence limit (95%) of 26.95 to 32.26 mg GAE/g (optimal conditions: 50 °C, 5 h and 100 mbar). Therefore, results of this study suggest a high content of phenolic compounds obtained from these by-products that show relevance as functional ingredients for application in the food, nutraceutical, and cosmeceutical industries.

Water Status and Predictive Models of Moisture Content during Drying of Soybean Dregs Based on LF-NMR

To explore the drying characteristics of soybean dregs and a nondestructive moisture content test method, in this study, soybean dregs were dried with hot air (80 °C), the moisture content was measured using the drying method, water status was analyzed using low-field nuclear magnetic resonance (LF-NMR) and the moisture content prediction models were built and validated. The results revealed that the moisture contents of the soybean dregs were 0.57 and 0.01 g/g(w.b.), respectively, after drying for 5 and 7 h. The effective moisture diffusivity increased with the decrease in moisture content; it ranged from 5.27 × 10^-9 to 6.96 × 10^-8 m²·s^-1. Soybean dregs contained bound water (T₂₁), immobilized water (T₂₂) and free water (T₂₃ and T₂₃'). With the proceeding of drying, all of the relaxation peaks shifted left until a new peak (T₂₃') appeared; then, the structure of soybean dregs changed, and the relaxation peaks reformed, and the peak shifted left again. The peak area may predict the moisture content of soybean dregs, and the gray values of images predict the moisture contents mainly composed of free water or immobilized water. The results may provide a reference for drying of soybean dregs and a new moisture detection method.