Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene Content

Abstract

Maturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation was applied to preprocess the hyperspectral data. Then, using competitive adaptive reweighted sampling (CARS), the characteristic wavelengths were selected to simplify the calibration models. Based on the full and characteristic wavelengths, a support vector classifier (SVC) model was developed to determine tomato maturity qualitatively. The results demonstrated that the classification accuracy using the characteristic wavelength led to the obtention of better results with an accuracy of 95.83%. In addition, the support vector regression (SVR) and partial least squares regression (PLSR) models were utilized to predict lycopene content. With a coefficient of determination for prediction (R2P) of 0.9652 and a root mean square error for prediction (RMSEP) of 0.0166 mg/kg, the SVR model exhibited the best quantitative prediction capacity based on the characteristic wavelengths. Following this, a visual distribution map was created to evaluate the lycopene content in tomato fruit intuitively. The results demonstrated the viability of hyperspectral imaging for detecting tomato maturity and quantitatively predicting the lycopene content during storage.

Rapid and nondestructive watermelon (Citrullus lanatus) seed viability detection based on visible near‐infrared hyperspectral imaging technology and machine learning algorithms

“…The HSI system's architecture is given in Figure 2 as was presented in our previous study (Dai et al, 2023;Nirere et al, 2022), and a model of spectral image acquisition mainly consists of an HSI camera (Zyla4.2, Andor, UTKL), computer and controller, and spectrometer (V10E, SPECIM) fiber optic halogen lamps source (UBerLED100, IT), electric displacement platform (MSI300, ISUZU OPTICS), black sample plate, and so on. Before the acquisition of spectral images, the apparatus went under a preheating period of 35 min, and while collecting HSIs, the samples were put on a dark piece of paper.…”

Application of hyperspectral imaging and colorimetric sensor array coupled with multivariate analysis for quality detection during salted duck eggs processing

“…As the cost of components decreases, it has potential for use in food and agriculture. Currently, this approach is being progressively used for studies on the quick detection and distribution of physicochemical markers in a range of agricultural and food goods, such as fruits, fish, and meat (Cheng, Sun, Yao, et al, 2023; Dai et al, 2023; Li, Tang, et al, 2023).…”