An online, non-destructive internal quality inspection/grading system was designed for Mengyin peaches using near-infrared spectroscopy. Online models to detect soluble solid content (SSC) and firmness of Mengyin peaches were established in keeping with the sorting system’s at a speed of 5 fruits/second. Spectra were pre-processed using methods including Savitzky-Golay smoothing (SGS) and Savitzky-Golay derivative (SG-DER) calculations. SSC and firmness prediction models were established for different wavelength ranges using partial least squares (PLS) fitting. The results showed that, in the construction of SSC prediction model, SG-DER pre-processing was optimum in the range of 600-750 nm and 750-900 nm wavelength. Correlation coefficients of calibration and validation sets were 0.919 and 0.863, and root mean square errors were 0.735 and 0.764%, respectively. SGS used for pre-processing was suitable in firmness prediction model construction. And the correlation coefficients of calibration and validation sets were 0.832 and 0.746, and root mean square errors were 0.774 N and 0.785 N, respectively. Furthermore, a genetic algorithm (GA) and a successive projections algorithm (SPA) were used to screen characteristic variables in the 600-750 nm and 750-900 nm wavelength ranges, and SSC and firmness prediction models were established with the respective use of SG-DER and SGS for spectral pre-preprocessing. The results revealed that both SPA and GA could effectively reduce the number of variables used in model construction. And they also could enhance the predictive ability and computation speed of online SSC and firmness prediction models. SSC and firmness prediction models established with SPA-PLS were better than those GA-PLS did. The correlation coefficient and root mean square error of the SSC prediction set were 0.916% and 0.721 %, respectively. Moreover, the correlation coefficient and root mean square error of the firmness prediction set were 0.811 and 0.742 N, respectively. Thus, on-line non-destructive detection of SSC and firmness of Mengyin peaches could be realized with near-infrared diffuse-transmission spectroscopy.
YU Huaizhi
,
CHEN Dongjie
,
JIANG Peihong
,
ZHANG Yuhua
,
GUO Fengjun
,
ZHANG Changfeng
. Online prediction of soluble solids and firmness of Mengyin peaches based on Vis/NIR diffuse-transmission spectroscopy[J]. Food and Fermentation Industries, 2020
, 46(14)
: 216
-221
.
DOI: 10.13995/j.cnki.11-1802/ts.022156
[1] ZHANG F, DONG P, FENG L, et al. Textural changes of yellow peach in pouches processed by high hydrostatic pressure and thermal processing during storage[J]. Food & Bioprocess Technology, 2012, 5(8):3 170-3 180.
[2] ZHANG F, ZHAO J, CHEN F, et al. Effects of high hydrostatic pressure processing on quality of yellow peaches in pouch[J]. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(6):337-343.
[3] 吴剑, 褚伟雄, 王晖. 热处理-冷藏对锦绣黄桃生理生化的影响及感官评价[J]. 食品科技, 2015(10):314-319.
[4] 余意,张鑫,张丽平,等.采收成熟度和贮藏温度对锦绣黄桃完熟品质的影响[J].食品工业科技,2015,36(4):334-338.
[5] SUN X, LIU Y , LI Y , et al. Simultaneous measurement of brown core and soluble solids content in pear by on-line visible and near infrared spectroscopy[J]. Postharvest Biology & Technology, 2016, 116(7):80-87.
[6] ZUDE M, HEROLD B, ROGER J M, et al. Non-destructive tests on the prediction of apple fruit flesh firmness and soluble solids content on tree and in shelf life[J]. Journal of Food Engineering, 2006, 77(2):254-260.
[7] HOBSON G E. The firmness of tomato fruit in relation to polygalacturonase activity[J]. Journal of Pomology & Horticultural Science, 2015, 40(1):66-72.
[8] GUO W C, DONG J L. Nondestructive detection on firmness of peaches based on hyperspectral imaging and artificial neural networks[J]. Optics & Precision Engineering, 2015, 23(6):1 530-1 537.
[9] KAR S, TUDU B, JANA A, et al. FT-NIR spectroscopy coupled with multivariate analysis for detection of starch adulteration in turmeric powder[J]. Food Additives & Contaminants Part A, 2019,36(6):863-875.
[10] BLANCO M, VILLARROYA I. NIR spectroscopy: A rapid-response analytical tool[J]. Trac Trends in Analytical Chemistry, 2002, 21(4):240-250.
[11] JIE D , XIE L , RAO X , et al. Using visible and near infrared diffuse transmittance technique to predict soluble solids content of watermelon in an on-line detection system[J]. Postharvest Biology & Technology, 2014, 90(4):1-6.
[12] 李跑,申汝佳,李尚科,等.一种基于近红外光谱与化学计量学的绿茶快速无损鉴别方法[J].光谱学与光谱分析,2019,39(8):2 584-2 589.
[13] 马文强,张漫,李源,等.核桃仁脂肪含量的近红外光谱无损检测[J].农业机械学报,2019,50(S1):374-379.
[14] 邹小波,张俊俊,黄晓玮,等.基于音频和近红外光谱融合技术的西瓜成熟度判别[J].农业工程学报,2019,35(9):301-307.
[15] 王铭海,郭文川,商亮,等.基于近红外漫反射光谱的多品种桃可溶性固形物的无损检测[J].西北农林科技大学学报(自然科学版),2014,42(2):142-148.
[16] BETEMPS D L, FACHINELLO J C, GALARÇA S P, et al. Spectroscopy of the visible and near infrared to evaluate soluble solids and flesh firmness in peach according to harvest season[J]. Semina Ciências Agrárias, 2014, 35(3):1 257-1 266.
[17] NASCIMENTO P A M, CARVALHO L C D, JÚNIOR L C C, et al. Robust PLS models for soluble solids content and firmness determination in low chilling peach using near-infrared spectroscopy (NIR)[J]. Postharvest Biology & Technology, 2016, 111:345-351.
[18] ARAÚJO M C U, SALDANHA T C B, GALVÃO R K H, et al. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis[J]. Chemometrics & Intelligent Laboratory Systems, 2001, 57(2):65-73.
[19] ARSLAN M, ZOU X, TAHIR H E, et al. Near-infrared spectroscopy coupled chemometric algorithms for prediction of antioxidant activity of black goji berries (Lycium ruthenicum Murr.)[J]. Journal of Food Measurement & Characterization, 2018, 12(4):1-11.
[20] 郭志明, 黄文倩, 陈全胜,等. 近红外光谱的苹果内部品质在线检测模型优化[J]. 现代食品科技, 2016(9):147-153.
[21] 田潇瑜, 黄星奕, 白竣文, 等. 基于近红外光谱技术的紫薯贮藏期间花青素含量检测[J]. 农业机械学报, 2019, 50(2):357-362.
