Detection on Firmness and Soluble Solid Content of Peach During Different Storage Days
LIU Yan-de, ZHANG Yu, JIANG Xiao-gang, SUN Xu-dong, XU Hai, LIU Hao-chen
School of Mechatronics & Vehicle Engineering, East China Jiaotong University, National and Local Joint Engineering Research Center of Fruit Intelligent Photoelectric Detection Technology and Equipment, Nanchang 330013, China
Abstract：Soluble solid content (SSC) and firmness are two important indexes of peach, which determine its internal quality. However, the water in the peach fruit is lost, the surface begins to soften and rot, and the internal quality changes during transportation or sale. This paper aims to investigate the feasibility of visible/near-infrared spectroscopy（VIS-NIR）in predicting SSC and firmness of peach during different storage days and to predict the optimal storage period of peaches further. The spectrum of peach in 4 storage stages was collected by diffuse transmittance and reflectance, and the sugar and hardness were measured. The mean spectrum of peach in four stages was analyzed. The spectral intensity increased with the storage days, and the peak shift was caused by the changes in the color and pigment of the peel in the region of 650~680 nm. Meanwhile, the changes in SSC and firmness were analyzed. The SSC gradually increased during storage, while the firmness rapidly decreased during storage. Finally, the SSC increased by 3.31% and the firmness decreased by 58.8%. Pretreatment methods such as multivariate scattering correction(MSC), S-G smoothing, normalization and baseline are used to reduce the impact of noise and errors in the spectrum, and uninformative variable elimination (UVE) and successive projections algorithm (SPA) is used to select characteristic wavelengths, then the partial least squares regression(PLS) is used to establish prediction models for SSC and firmness. Analyzing the PLS regression coefficient of SSC and firmness with the mean spectrum, it is found that SSC has many high regression coefficient bands, and the high regression coefficient of firmness is near the peaks and troughs. Therefore, the SSC model established by the characteristic wavelength obtained by SPA and UVE is not good, while the firmness model is good. The results show that the best prediction correlation coefficient (Rp) and root mean square error of prediction (RMSEP) of SSC under the diffuse transmittance and reflectance detection methods are 0.886, 0.727, 0.820, 1.003, respectively. The pretreatment methods are MSC and S-G smoothing with 3 smoothing window width, respectively. In addition, the SPA-PLS model of firmness established by diffuse transmittance uses 15 spectral variables to obtain Rp and RMSEP of 0.798 and 0.976. The UVE-PLS model established by the diffuse reflectance uses 113 spectral variables to obtain Rp and RMSEP of 0.841 and 0.829. It can be seen that the diffuse transmittance method predicts SSC better, and the diffuse reflectance predicts the firmness better during peach storage. The SSC and firmness prediction models established by VIS-NIR can reliably predict the changes of SSC and firmness during the storage of peaches and have certain reference value to guide picking and selling time and reduce decay.
Key words：Visible/Near infrared spectroscopy; Storage of peach; Soluble solid content and firmness; Partial least squares regression
刘燕德，张 雨，姜小刚，孙旭东，徐 海，刘昊辰. 不同贮藏期水蜜桃硬度及糖度的检测研究[J]. 光谱学与光谱分析, 2021, 41(01): 243-249.
LIU Yan-de, ZHANG Yu, JIANG Xiao-gang, SUN Xu-dong, XU Hai, LIU Hao-chen. Detection on Firmness and Soluble Solid Content of Peach During Different Storage Days. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 243-249.
[1] Pan L, Lu R, Zhu Q, et al. Postharvest Biology and Technology, 2015, 102: 42.
[2] Wang A, Xie L. Journal of Food Engineering, 2014, 143(12): 17.
[3] Moscetti R, Raponi F, Ferri S, et al. Journal of Food Engineering, 2018, 222: 139.
[4] Zhu G, Tian C. Journal of Food Process Engineering, 2018, 41(6): e12810.
[5] PAN Lei-qing, LIU Ming, HANG Dong-hai, et al(潘磊庆, 刘 明, 韩东海, 等). Journal of Nanjing Agricultural University(南京农业大学学报), 2013, 36(4): 116.
[6] Huang Y, Lu R, Chen K. Journal of Food Engineering, 2018, 222: 185.
[7] Uwadaira Y, Sekiyama Y, Ikehata A. Heliyon, 2018, 4(2): e00531.
[8] GUO Wen-chuan, WANG Ming-hai, GU Jing-si, et al(郭文川, 王铭海, 谷静思, 等). Optics and Precision Engineering(光学精密工程), 2013, 21(10): 2721.
[9] Wang H, Peng J, Xie C, et al. Sensors, 2015, 15(5): 11889.
[10] Mahesh S, Jayas D S, Paliwal J, et al. Food and Bioprocess Technology, 2015, 8(1): 31.
[11] Li J, Wang Q, Xu L, et al. Food Analytical Methods, 2019, 12(1): 12.
[12] Tian X, Fan S, Li J, et al. Infrared Physics & Technology, 2019, 102: 102979.
[13] Maeda H, Ozaki Y, Tanaka M, et al. Journal of Near Infrared Spectroscopy, 1995, 3(4): 191.
