Detection of internal qualities of hami melons using hyperspectral imaging technology based on variable selection algorithms

Abstract

Hyperspectral imaging technology was used to assess the soluble solids content (SSC), titrable acidity content (TAC), and firmness of hami melons. The mean spectra were extracted from the regions of interest (ROI) of the hyperspectral images of each hami melon. Spectral data were first pretreated with different preprocessing methods and analyzed using the partial least squares (PLS) method to build calibration models. However, full spectral data contain a great number of redundant and colinear variables that lead to poor predicting ability of calibration models. Three typical variable selection methods, i.e., the genetic algorithm (GA), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS) were used to extract effective wavelength variables from the full spectra for determination of SSC, TAC, and firmness of hami melon. The results showed that the SNV-CARS-PLS model based on 51 and 40 wavelength variables achieved the optimal performance for SSC and TAC compared with full spectral SNV-PLS, SNV-GA-PLS, and SNV-SPA-PLS models. The R P , RMSEP, and RPD of SNV-CARS-PLS model for SSC were 0.9606, 0.3816, and 3.598, respectively, and 0.9125, 0.0263, and 2.445 for TAC, respectively. The best model for predicting firmness was RAW-CARS-PLS model based on 57 wavelength variables with the R P RMSEP, and RDP values of 0.8671, 20.05, and 1.996, respectively. Overall, the results indicate that the CARS is a powerful method for selecting effective wavelength variables and demonstrated the feasibility of using hyperspectral imaging technology as a fast and nondestructive method for simultaneous detection of SSC, TAC, and firmness of hami melons. Practical applicationsThe quality of melon has a direct relationship with the soluble solids content, titrable acidity content, and texture of melon. To ensure the best taste and highest nutritional value, it is better for melons to be harvested when completely mature. Nowadays, melons were harvested according to different fruit development times after flowering. Traditional methods for detection of internal qualities and maturity of melon mainly focus on the appearance of melon and depend on the feelings of professionals, which are tedious, time-consuming, expensive, and greatly influenced by subjective factors. More than anything, Traditional methods are hard to be highly accurate. Hyperspectral imaging technology has the advantages of rapid and nondestructive. The results showed that hyperspectral imaging technology for the simultaneous detection of soluble solids content, titrable acidity content, and firmness of melon is feasible. And the results can provide a reference for detecting large fruits by using hyperspectral imaging technology

Determination of soluble solids content and firmness in plum using hyperspectral imaging and chemometric algorithms

“…In recent years, the nondestructive detection methods for SSC and/or firmness of fruits have been presented (Arendse et al, 2018;Li et al, 2016;Sun et al, 2016). Among them, hyperspectral imaging technology has been widely used to nondestructively determine SSC and/or firmness of fruits, that is, apple (Dong et al, 2016;Hou et al, 2018), pear (Fan et al, 2015;Yu et al, 2018), orange (Zhang et al, 2020), kiwifruit (Hu et al, 2017), blueberries (Leiva-Valenzuela et al, 2013), melon (Zhang et al, 2018), banana (Rajkumar et al, 2012) and lychee (Pu et al, 2016).…”

Integration of Artificial Neural Network Modeling and Hyperspectral Data Preprocessing for Discrimination of Colla Corii Asini Adulteration

“…The CWs mainly concentrated at the region from 700 to 800 nm. It was consistent with absorption peak appearing in Figure 2, which was attributed to the third and fourth overtone of the O-H-and C-H-functional group, respectively [39]. The hyperspectral images of CWs were applied to extracted image features which were further used for prediction models.…”

“…The apparent absorption peaks at approximately 760 nm appeared on the ARS curves. The 700-780 nm region presented one obvious absorption peak particularly, which could be attributed to the O-H third overtones and C-H forth overtones [38,39]. Because too much noise existed in region of 388-400 nm and 1000-1045 nm in the original ARS and corrected images, the remaining region from 400 to 1000 nm was employed for further analysis.…”

A simple multiple linear regression model in near infrared spectroscopy for soluble solids content of pomegranate arils based on stability competitive adaptive re-weighted sampling

“…11 With increased understanding of NIR spectroscopy, it is found that eliminating redundant variables from the full spectrum can not only simplify the near infrared spectral analysis models, but can also improve the interpretability, the prediction effect and the robustness of the model. 12 The effectiveness of variable selection has been widely verified in various NIR spectroscopic applications in the detection of fruits, such as soluble solids content (SSC) of apple, 13 pear, 14 watermelon, 15 beer, 16 navel oranges, 17 hami melons, 18 and SSC on-line determination of intact apple. 19 Based upon variable selection, the number of variables can be greatly reduced, and the establishment of multivariate calibration model becomes shorter.…”