SSC prediction of cherry tomatoes based on IRIV‐CS‐SVR model and near infrared reflectance spectroscopy

Abstract

As one of the most important indexes of internal quality testing of fruit, soluble solids content (SSC) is significant for its rapid and efficient nondestructive testing by using near infrared reflectance spectroscopy (NIRS). In this article, 126 cherry tomatoes were selected as the research object. Reflectance spectra data of 228 bands in cherry tomatoes were acquired by the near infrared spectrometer and SSC was measured by the hand‐held refractometer. Savitzky–Golay (SG) combined with multiplicative scatter correction (MSC) was used to preprocess the spectral data to reduce the effects of light scattering and other noise. Then, the dimensions of spectral data were reduced by iteratively retaining informative variables (IRIV) algorithm and 10 characteristic wavelengths were obtained, which were 1,080.37, 1,113.62, 1,117.3, 1,297.57, 1,301.02, 1,538.32, 1,540.40, 1,590.72, 1,615.94, and 1,636.89 nm, respectively. Subsequently, support vector regression (SVR) and its two optimization models, PSO‐SVR and CS‐SVR, were respectively used to establish SSC prediction models based on full spectra and characteristic spectra. The experimental results showed the IRIV‐CS‐SVR model for SSC prediction achieved the accuracy with RP2 of 0.9718 and RC2 of 0.9845. Thus, it is feasible to use NIRS with IRIV‐CS‐SVR to make a rapid and efficient nondestructive SSC prediction of cherry tomatoes. Practical applications As one of the important testing standards of fruit internal quality, SSC is of great significance for the rapid and efficient nondestructive testing. In this article, an iteratively retaining information variables (IRIV) algorithm is proposed to extract the characteristic wavelengths, and a regression model CS‐SVR is established by combining the optimization algorithm cuckoo search (CS). This study shows that the model IRIV‐CS‐SVR has a certain effect on SSC prediction of cherry tomatoes.

QTL Mapping and GWAS Reveal the Genetic Mechanism Controlling Soluble Solids Content in Brassica napus Shoots

“…Based on the phenotypic data, we calculated mean SSC values of 5.89 • Brix and 6.98 • Brix for the AH and GWAS populations, respectively, and compared them with other crops. Natalia et al [59] measured the average SSC of the external, middle and internal parts of the celery petioles with a refractometer and ranged from 3.49 • Brix to 3.74 • Brix with an average of 3.61 • Brix; Kramchote et al [60] measured the SSC ranging from 3.45 • Brix to 5.23 • Brix with a mean of about 4.34 • Brix in 135 cabbage samples; the SSC of 126 cherry tomatoes was measured by Sun et al [61] and ranged from 5.0-8.5 • Brix with a mean of 6.6 • Brix; and Li et al [62] measured the SSC of stylar-end and equatorial locations of 360 melons, with average values ranging from 7.88 • Brix to 10.72 • Brix. It can be seen that the average SSC in B. napus shoots was higher than that of celery and cabbage, lower than that of melon, and approximately equal to the cherry tomato.…”

Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms

“…KNN, SVM, LDA, and decision tree were used to establish maize seed varieties. IRIV has good feature extraction ability for high-dimensional data, and its use has not yet been reported for maize seed variety identification ( Xu et al, 2021 ; Sun et al, 2018b ; Yun et al, 2014 ; Sun et al, 2018a ). Based on different pre-processing and characteristic band extraction methods, maize seed variety identification models based on the base learner are quite different.…”

Soluble Solids Content Binary Classification of Miyagawa Satsuma in Chongming Island Based on Near Infrared Spectroscopy

“…The results indicated that the model built using 18 effective variables achieved the optimal performance for the prediction of SSC. Jun et al ( 2018 ) used an iteratively retaining informative variables algorithm to obtain 10 characteristic wavelengths when processing samples in predicting the SSC of cherry tomatoes. The experimental results showed the IRIV−CS−SVR model for SSC prediction could reach accuracy with = 0.9718 and = 0.9845.…”