Hyperspectral Imaging (HSI) Technology for the Non-Destructive Freshness Assessment of Pearl Gentian Grouper under Different Storage Conditions

Simultaneous determination of pigments of spinach (Spinacia oleracea L.) leaf for quality inspection using hyperspectral imaging and multi-task deep learning regression approaches

Rapid and accurate determination of pigment content is important for quality inspection of spinach leaves during storage. This study aimed to use hyperspectral imaging at two spectral ranges (visible/near-infrared, VNIR: 400-1000 nm; NIR: 900-1700 nm) to simultaneously determine the pigment (chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids) content in spinach stored at different durations and conditions (unpackaged and packaged). Partial least squares (PLS), back propagation neural network (BPNN) and convolutional neural network (CNN) were used to establish single-task and multi-task regression models. Single-task CNN (STCNN) models and multi-task CNN (MTCNN) models obtained better performances than the other models. The models using VNIR spectra were superior to those using NIR spectra. The overall results indicated that hyperspectral imaging with multi-task learning could predict the quality attributes of spinach simultaneously for spinach quality inspection under various storage conditions. This research will guide food quality inspection by simultaneously inspecting multiple quality attributes.

Developing a Portable Fluorescence Imaging Device for Fish Freshness Detection

Rapid detection of fish freshness is of vital importance to ensuring the safety of aquatic product consumption. Currently, the widely used optical detecting methods of fish freshness are faced with multiple challenges, including low detecting efficiency, high cost, large size and low integration of detecting equipment. This research aims to address these issues by developing a low-cost portable fluorescence imaging device for rapid fish freshness detection. The developed device employs ultraviolet-light-emitting diode (UV-LED) lamp beads (365 nm, 10 W) as excitation light sources, and a low-cost field programmable gate array (FPGA) board (model: ZYNQ XC7Z020) as the master control unit. The fluorescence images captured by a complementary metal oxide semiconductor (CMOS) camera are processed by the YOLOv4-Tiny model embedded in FPGA to obtain the ultimate results of fish freshness. The circuit for the YOLOv4-Tiny model is optimized to make full use of FPGA resources and to increase computing efficiency. The performance of the device is evaluated by using grass carp fillets as the research object. The average accuracy of freshness detection reaches up to 97.10%. Moreover, the detection time of below 1 s per sample and the overall power consumption of 47.1 W (including 42.4 W light source power consumption) indicate that the device has good real-time performance and low power consumption. The research provides a potential tool for fish freshness evaluation in a low-cost and rapid manner.

Quality Evaluation of Mackerel Fillets Stored under Different Conditions by Hyperspectral Imaging Analysis

This study was designed to compare the quality changes in mackerel fillets stored under different conditions by using hyperspectral imaging (HSI) techniques. Fillets packaged in vacuum were stored for six days under five different conditions: refrigerated at 4°C (R group); iced at 5±3°C (I group); kept at an ambient of 17±2°C (A group); frozen at -18°C for 24 h and thawed in a refrigerator at 4°C for 5 h on the sampling day (FTR group); FTR thawed in tap water instead of thawing in a refrigerator (FTW group). The FTR group had the lowest total bacterial count, drip loss, 2-thiobarbituric acid reactive substances, volatile basic nitrogen, and texture profile analysis values among groups during the entire storage period (p<0.05). Scanning electron microscopy revealed that the FTR group had less damage, while the other groups had shrunken muscle tissues. HSI integrated with the partial least squares model yielded reliable and efficient results, with high R2cv values, for several quality parameters of the mackerel fillets. Overall, the FTR group, involving freezing and thawing in a refrigerator, appears to be the most favorable option for maintaining the quality of mackerel fillets, which could be practically implemented in the industry. HSI is a suitable and effective technique for determining the quality of mackerel fillets stored under different conditions.

Rapid Detection of Different Types of Soil Nitrogen Using Near-Infrared Hyperspectral Imaging

Rapid and accurate determination of soil nitrogen supply capacity by detecting nitrogen content plays an important role in guiding agricultural production activities. In this study, near-infrared hyperspectral imaging (NIR-HSI) combined with two spectral preprocessing algorithms, two characteristic wavelength selection algorithms and two machine learning algorithms were applied to determine the content of soil nitrogen. Two types of soils (laterite and loess, collected in 2020) and three types of nitrogen fertilizers, namely, ammonium bicarbonate (ammonium nitrogen, NH₄-N), sodium nitrate (nitrate nitrogen, NO₃-N) and urea (urea nitrogen, urea-N), were studied. The NIR characteristic peaks of three types of nitrogen were assigned and regression models were established. By comparing the model average performance indexes after 100 runs, the best model suitable for the detection of nitrogen in different types was obtained. For NH₄-N, R²_p = 0.92, RMSE_P = 0.77% and RPD = 3.63; for NO₃-N, R²_p = 0.92, RMSE_P = 0.74% and RPD = 4.17; for urea-N, R²_p = 0.96, RMSE_P = 0.57% and RPD = 5.24. It can therefore be concluded that HSI spectroscopy combined with multivariate models is suitable for the high-precision detection of various soil N in soils. This study provided a research basis for the development of precision agriculture in the future.