Using hyperspectral imaging technology for assessing internal quality parameters of persimmon fruits during the drying process

A hyperspectral deep learning attention model for predicting lettuce chlorophyll content

BACKGROUND

The phenotypic traits of leaves are the direct reflection of the agronomic traits in the growth process of leafy vegetables, which plays a vital role in the selection of high-quality leafy vegetable varieties. The current image-based phenotypic traits extraction research mainly focuses on the morphological and structural traits of plants or leaves, and there are few studies on the phenotypes of physiological traits of leaves. The current research has developed a deep learning model aimed at predicting the total chlorophyll of greenhouse lettuce directly from the full spectrum of hyperspectral images.

RESULTS

A CNN-based one-dimensional deep learning model with spectral attention module was utilized for the estimate of the total chlorophyll of greenhouse lettuce from the full spectrum of hyperspectral images. Experimental results demonstrate that the deep neural network with spectral attention module outperformed the existing standard approaches, including partial least squares regression (PLSR) and random forest (RF), with an average R2 of 0.746 and an average RMSE of 2.018.

CONCLUSIONS

This study unveils the capability of leveraging deep attention networks and hyperspectral imaging for estimating lettuce chlorophyll levels. This approach offers a convenient, non-destructive, and effective estimation method for the automatic monitoring and production management of leafy vegetables.

Predictive Analysis of Linoleic Acid in Red Meat Employing Advanced Ensemble Models of Bayesian and CNN-Bi-LSTM Decision Layer Fusion Based Hyperspectral Imaging

Rapid non-destructive testing technologies are effectively used to analyze and evaluate the linoleic acid content while processing fresh meat products. In current study, hyperspectral imaging (HSI) technology was combined with deep learning optimization algorithm to model and analyze the linoleic acid content in 252 mixed red meat samples. A comparative study was conducted by experimenting mixed sample data preprocessing methods and feature wavelength extraction methods depending on the distribution of linoleic acid content. Initially, convolutional neural network Bi-directional long short-term memory (CNN-Bi-LSTM) model was constructed to reduce the loss of the fully connected layer extracted feature information and optimize the prediction effect. In addition, the prediction process of overfitting phenomenon in the CNN-Bi-LSTM model was also targeted. The Bayesian-CNN-Bi-LSTM (Bayes-CNN-Bi-LSTM) model was proposed to improve the linoleic acid prediction in red meat through iterative optimization of Gaussian process acceleration function. Results showed that best preprocessing effect was achieved by using the detrending algorithm, while 11 feature wavelengths extracted by variable combination population analysis (VCPA) method effectively contained characteristic group information of linoleic acid. The Bi-directional LSTM (Bi-LSTM) model combined with the feature extraction data set of VCPA method predicted 0.860 Rp2 value of linoleic acid content in red meat. The CNN-Bi-LSTM model achieved an Rp2 of 0.889, and the optimized Bayes-CNN-Bi-LSTM model was constructed to achieve the best prediction with an Rp2 of 0.909. This study provided a reference for the rapid synchronous detection of mixed sample indicators, and a theoretical basis for the development of hyperspectral on-line detection equipment.

Non-destructive monitoring of quality attributes kinetics during the drying process: A case study of celeriac slices and the model generalisation in selected commodities

The potential of Visual-NIR hyperspectral imaging (VNIR-HSI, 425-1700 nm) to predict celeriac quality attributes during the drying process was investigated. The HSI-Gaussian Process Regression (GPR) fusion method excellently predicted moisture content (MC, R² ≈ 1.00, RMSE = 0.77 g_w 100 g_s^-1) and water activity (a_w, R² = 0.98, RMSE = 0.04). Moreover, the rehydration ratio (RR, R² = 0.89, RMSE = 0.04) and colour indices (R² = 0.80-0.93, RMSE = 0.17-1.45) were reasonably predicted. However, antioxidant activity (AA) and total phenolic compounds (TPC) were poorly predicted. These results are potentially due to MC variations dominating the NIR region, masking phenolic compounds. Finally, the celeriac-based-trained model was assessed by predicting the MC of apple, cocoyam, and carrot slices. The results were encouraging; however, a GPR model trained on the data of all four commodities was more robust (R² ≈ 1.00, RMSE = 1-2 g_w 100 g_s^-1).

Combination of hyperspectral imaging and entropy weight method for the comprehensive assessment of antioxidant enzyme activity in Tan mutton

The antioxidant enzymes play the crucial role in inhibiting mutton spoilage. In this study, visible near-infrared (Vis-NIR) hyperspectral imaging (HSI) combined with entropy weight method (EWM) was developed for the first time to evaluate the antioxidant properties of Tan mutton. The comprehensive index of antioxidant enzymes (AECI) consisting of peroxidase (49.34%), catalase (37.97%) and superoxidase (12.69%) was constructed by the EWM. Partial least squares regression, least squares support vector machine and artificial neural networks (ANN) were developed based on characteristic wavelengths extracted by successful projections algorithm, uninformative variable selection, iteratively retains informative variables (IRIV), regression coefficient and competitive adaptive reweighted sampling (CARS). The textural features (TF) were extracted by the gray level co-occurrence matrix and fused with the spectral data to establish models. Visualization of the changes in antioxidant enzyme activity was constructed from the optimal model. In addition, two-dimensional correlation spectra (2D-COS) with AECI as a perturbation variable was used to identify spectral features, revealing chemical bond changes order under the characteristic peaks at 612-799-473-708-559 nm. The results showed that the IRIV-CARS-TF-ANN model performed the best, with prediction set coefficient of determination (R_P²) of 0.8813, which improved 2.12%, 1.11% and 2.77% over the R_P² of full band, IRIV and IRIV-CARS, respectively. It was suggested that fusion data of HSI may effectively predict the activity of antioxidant enzymes in Tan mutton.

The Application of Hyperspectral Imaging Technologies for the Prediction and Measurement of the Moisture Content of Various Agricultural Crops during the Drying Process

Drying is one of the common procedures in the food processing steps. The moisture content (MC) is also of crucial significance in the evaluation of the drying technique and quality of the final product. However, conventional MC evaluation methods suffer from several drawbacks, such as long processing time, destruction of the sample and the inability to determine the moisture of single grain samples. In this regard, the technology and knowledge of hyperspectral imaging (HSI) were addressed first. Then, the reports on the use of this technology as a rapid, non-destructive, and precise method were explored for the prediction and detection of the MC of crops during their drying process. After spectrometry, researchers have employed various pre-processing and merging data techniques to decrease and eliminate spectral noise. Then, diverse methods such as linear and multiple regressions and machine learning were used to model and predict the MC. Finally, the best wavelength capable of precise estimation of the MC was reported. Investigation of the previous studies revealed that HSI technology could be employed as a valuable technique to precisely control the drying process. Smart dryers are expected to be commercialised and industrialised soon by the development of portable systems capable of an online MC measurement.

Citral Essential Oil-Loaded Microcapsules by Simple Coacervation and Its Application on Peach Preservation

Citral essential oil (CEO) was encapsulated by the single coalescence method, and its stability, release properties, and ability to maintain freshness were evaluated for the first time. The microshape characteristics of a CEO-loaded microcapsule (CM) were analyzed by inverted microscopy (OM) and scanning electron microscopy (SEM). The encapsulation efficiency, stability, and release behavior of CEO were evaluated using Fourier transform infrared spectroscopy (FTIR), thermogravimetric/differential thermal comprehensive analysis (TG/DSC), and gas chromatography mass spectrometry (GC/MS). Moreover, peaches were used to evaluate the preservation properties of the CEO-loaded microcapsule. The results showed that the microcapsule produced using simple coacervation had better microstructure and the ability to reduce and control the release of citral essential oil. The qualities of peaches, such as appearance changes, hardness, soluble solid content, total acids, and total bacterial counts, were significantly improved in the CM system during storage, in comparison with the control and cold storage groups. Therefore, the CM has potential applications and development prospects in the food, drug, and other industries.

Real-Time Monitoring of the Quality Changes in Shrimp (Penaeus vannamei) with Hyperspectral Imaging Technology during Hot Air Drying

Hot air drying is the most common processing method to extend shrimp’s shelf life. Real-time monitoring of moisture content, color, and texture during the drying process is important to ensure product quality. In this study, hyperspectral imaging technology was employed to acquire images of 104 shrimp samples at different drying levels. The water distribution and migration were monitored by low field magnetic resonance and the correlation between water distribution and other quality indicators were determined by Pearson correlation analysis. Then, spectra were extracted and competitive adaptive reweighting sampling was used to optimize characteristic variables. The grey-scale co-occurrence matrix and color moments were used to extract the textural and color information from the images. Subsequently, partial least squares regression and least squares support vector machine (LSSVM) models were established based on full-band spectra, characteristic spectra, image information, and fused information. For moisture, the LSSVM model based on full-band spectra performed the best, with residual predictive deviation (RPD) of 2.814. For L*, a*, b*, hardness, and elasticity, the optimal models were established by LSSVM based on fused information, with RPD of 3.292, 2.753, 3.211, 2.807, and 2.842. The study provided an in situ and real-time alternative to monitor quality changes of dried shrimps.

Pulsed Vacuum Drying of Persimmon Slices: Drying Kinetics, Physicochemical Properties, Microstructure and Antioxidant Capacity

In order to explore an alternative drying method to enhance the drying process and quality of persimmon slices, pulsed vacuum drying (PVD) was employed and the effects of different drying temperatures (60, 65, 70, and 75 °C) on drying kinetics, color, rehydration ratio (RR), microstructure, bioactive compounds, and the antioxidant capacity of sliced persimmons were investigated in the current work. Results showed that the rehydration ratio (RR) of the samples under PVD was significantly higher than that of the traditional hot air-dried ones. Compared to the fresh samples, the dried persimmon slices indicated a decrease in the bioactive compounds and antioxidant capacity. The total phenolic content (TPC) of PVD samples at 70 °C was 87.96% higher than that of the hot air-dried persimmon slices at 65 °C. Interestingly, at 70 °C, the soluble tannin content and TPC of the PVD samples reached the maximum values of 6.09 and 6.97 mg GAE/g, respectively. The findings in the current work indicate that PVD is a promising drying method for persimmon slices as it not only enhances the drying process but also the quality attributes.