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| Determination of Soluble Solid Content in Peach Based on Hyperspectral Combination With BPSO |
| ZHANG Li-xiu, ZHANG Shu-juan*, SUN Hai-xia, XUE Jian-xin, JING Jian-ping, CUI Tian-yu |
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
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Abstract Soluble solids (SSC) are an important index to evaluate the internal quality of Kubo peach. Traditional SSC content detection is destructive, time-consuming and laborious. Rapid and nondestructive detection of the SSC content of Kubo peach is of great importance for its quality classification. Binary particle swarm optimization (BPSO) is obtained by updating the speed formula based on standard particle swarm optimization (PSO). BPSO has the characteristics of high accuracy and fast convergence and is mostly used in optimization problems in separate spaces. Taking Kubo peach as the research object. Basedon hyperspectral technology combined with BPSO and based on BPSO combined characteristic wavelength selection algorithm to study the SSC content of Kubo peach. Firstly,hyperspectral information of 198 Kubo peaches was collected to obtain the spectral curve of Kubo peaches in the range of 900~1 700 nm. Meanwhile, theSSC value of Kubo peaches was. Used (Kennard-stone) algorithm to divide samples into a correction set (147) and a prediction set (51). The BPSO feature wavelength selection algorithm is used to extract the feature wavelength from Kubo's original spectral data. It is compared with the Competitive Adaptive Reweighting algorithm (CARS), Successive projections algorithm (SPA), and Uninformative variable selection algorithm (UVE). A method of extracting characteristic wavelength based on BPSO is proposed for primary combination (BPS0+CARS, BPSO+SPA, BPSO+UVE) and secondary combination ((BPSO+ CARS)-SPA), (BPSO+SPA)-SPA), (BPSO+UVE)-SPA). Based on the10 characteristic wavelength extraction methods above. Established support vector machine (LS-SVM) model and the genetic algorithm (GA) optimized support vector machine (GA-SVM) model of Kubo peach SSC content. The results show that the prediction performance of the model based on the BPSO algorithm is higher than that of other single characteristic wavelength algorithm, and the coefficient of determination R2p of the prediction set of the two models is above 0.97. Among the combination algorithms based on BPSO, the LS-SVM based on the quadratic combination (BPSO+SPA)-SPA algorithm has the highest prediction performance for Kubo peach SSC content when the number of characteristic wavelengths is small. The coefficient of determination between the correction set and the prediction set are 0.982 and 0.955, respectively. The root mean square errors RMSEC and RMSEP were 0.108 and 0.139, respectively. The prediction performance of the proposed model is slightly lower than that of the BPSO algorithm, but only 22 characteristic wavelengths are used for modeling, which greatly simplifies the model. These results show that (BPSO+SPA)-SPA is an effective method for extracting characteristic wavelength, which provides a new method for nondestructive detection of fruit SSC content.
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Received: 2022-09-16
Accepted: 2022-11-17
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Corresponding Authors:
ZHANG Shu-juan
E-mail: zsujuan1@163.com
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