The efficiency of hyper- and multispectral imaging (HSI and MSI) has gained considerable attention in research on plant phenotyping. This is due to their ease of use while being considered a nondestructive technology. Unlike current point-scanned spectroscopy, both HSI and MSI extract spatial and spectral information while covering a wide range of a plant body. Moreover, it is necessary to equip the extracted information with multivariate calibration techniques, followed by model evaluation. To date, the application of HSI and MSI for monitoring plant growth under a controlled environment is emerging and showing a good trend. Our systematic literature review discusses spectroscopy imaging techniques and their chemometric approaches as a sustainable sensor technology to detect plant traits. In conclusion, we also explore the possibility of carrying out HSI and MSI during plant trait analysis.

The interest in traditional meat being replaced by plant-based food has increased throughout the years. Some agricultural products, such as root crops, could be incorporated into alternative meat products due to the health benefits. However, relevant studies have discovered that some roots are considered allergen materials, necessitating further identification to maintain consumer safety. Aside from high accuracy, the limitations offered by traditional identification methods are a reason to employ nondestructive methods. This study aimed to develop a hyperspectral imaging system measuring the 400 nm to 1000 nm spectral range for the nondestructive identification of roots in soybean-based patty. Four thin-sliced traditional medicinal roots (tianma (Gastrodia elata), balloon flower root (Platycodon grandiflorum), deodeok (Codonopsis lanceolata), and ginseng (Panax ginseng)) were incorporated in a soybean-based patty with a concentration of 5% w/w. Moreover, support vector machine (SVM) learning and one-dimensional convolutional neural networks (1D-CNN) were realized for the discrimination model in tandem with spectral data extracted from the hyperspectral image. Our study demonstrated that SVM learning effectively discriminates between original patty and patty with root addition, with an F1-score, precision, and recall beyond 96.77%. This optimum model was achieved by using the standard normal variate (SNV) spectra.

In this study, we explored the potential of the Maillard reaction-based time-temperature indicators (TTI) as a tool for predicting and visualizing moisture variations during high-temperature drying. Using activation energy analysis, we found that the Maillard reaction-based TTI could not only visualize but also predict changes in moisture contents during high-temperature drying of 60-80°C. The color changes of the Maillard reaction solutions were distinct enough to be discerned with the naked eye, transitioning from colorless to black via the shift of yellow, light brown, brown, and dark brown. The dynamic characteristics for the color change in the Maillard reaction solutions and the moisture changes in the drying of thin-layer apples could be expressed with high suitability using a logistic model. This suggests that the Maillard reaction-based TTI can potentially be a practical and reliable tool for predicting the moisture changes for the high-temperature drying of thin-layer apples, offering a promising avenue for future research and applications.