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| Quantitative Analysis of Soluble Solids and Titratable Acidity Content in Angeleno Plum by Near-Infrared Spectroscopy With BP-ANN and PLS |
| ZHAO Zhi-lei1, 2, 3, 4，WANG Xue-mei1, 2, 3，LIU Dong-dong1, 2, 3，WANG Yan-wei1, 2, 3，GU Yu-hong5，TENG Jia-xin1，NIU Xiao-ying1, 2, 3, 4* |
1. College of Quality and Technical Supervision, Hebei University, Baoding 071002, China
2. National & Local Joint Engineering Research Center of Metrology Instrument and System, Hebei University, Baoding 071002, China
3. Hebei Key Laboratory of Energy Metering and Safety Testing Technology, Hebei University, Baoding 071002, China
4. Institute of Geographical Indications，Hebei University, Baoding 071002, China
5. College of Life Science, Hebei Agricultural University, Baoding 071002, China
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Abstract Soluble solid content (SSC) and titratable acidity (TA) are important indexes affecting the fruit quality and the fruit quality grading. Classical destructive detection methods are not suitable for fruit classification by quality. NIRS detection method is fast, easy to operate and can detect fruit quality without damage. In order to achieve non-destructive and rapid determination of SSC and TA in Angeleno plum fruits by near-infrared spectroscopy (NIR), diffuse reflectance spectra of plum fruits were collected by NIR spectrometer, SSC was measured by saccharometer, and TA content was determined by titration. Using leverage and F probability value to eliminate abnormal samples and software optimization combined with a manual screening of spectral bands, eliminating constant offset, subtracting a straight line, standard normal variate (SNV), max-minimum normalization, and multiplicative scatter correction （MSC), first and the second derivative combined smoothing, the first derivative combined minus a straight line and smoothing, and the first derivative combined with SNV or MSC correction. Partial least squares (PLS) and back propagation artificial neural network (BP-ANN) were used to establish the quantitative models of SSC and TA of plum fruit. Results indicated that the best Band ranges of plum fruit SSC and TA are 4 000～8 852 and 4 605～6 523 cm-1 respectively. The best spectral preprocessing method of the PLS model of SSC was MSC correction. The best model correction correlation coefficient (Rc) was 0.914 4, the prediction correlation coefficient (Rp) was 0.878 5, the correction root means square error (RMSEC) was 0.91, and the prediction root means square error (RMSEP) was 1.00. After the first order differential combined with SNV and 9-point smoothing, the PLS model of TA was the best, and the Rc, Rp, RMSEC and RMSEP were 0.860 3, 0.819 6, 0.80 and 0.86. The principal components of SSC and TA spectral data of plum fruits were extracted, and the optimal BP-ANN quantitative analysis model of SSC and TA were established based on the first 10 principal component scores. The SSC BP-ANN model’s Rc, Rp, RMSEC and RMSEP were 0.976 7, 0.889 7, 0.75 and 0.99. The corresponding parameter values of the BP-ANN model of TA were 0.974 3, 0.897 7, 0.62 and 0.83, respectively. Compared with the quantitative model established by the PLS algorithm, the BP-ANN model has higher Rc and Rp and lower RMSEC and RMSEP than that of the PLS algorithm, so the quantitative analysis results from the BP-ANN model were better than that of the PLS algorithm for SSC and TA indicators.
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Received: 2021-08-22
Accepted: 2022-03-17
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Corresponding Authors:
NIU Xiao-ying
E-mail: 408643620@qq.com
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[1] Lan Weijie，Jaillais B, Leca A, et al. Food Chemistry, 2020, 310: 125944.
[2] Wang J, Wang J, Chen Z，et al. Postharvest Biology and Technology, 2017, 129: 143.
[3] Shah S, Zeb A, Qureshi W S，et al. Infrared Physics & Technology, 2021, 103639.
[4] Paloma Andrade Martins Nascimento，et al. Postharvest Biology and Technology, 2016, 111: 345.
[5] QIANG Feng, WANG Qin-zhi, HE Jian-guo, et al（强 锋, 王芹志, 何建国, 等）. Modern Food Science & Technology（现代食品科技）, 2017, 212(4): 283.
[6] Cicooritti R, Paliotta M, Tiziana A，et al. Scientia Horticulturae, 2019, 257.
[7] JIANG Shui-quan, SUN Tong（江水泉, 孙 通）. Food & Machinery（食品与机械）, 2020, 36(2): 89.
[8] Maniwara P, Nakano K, Ohashi S，et al. Scientia Horticulturae, 2019, 257.
[9] Ar N H, Purwanto Y A，Budiastra I W et al. IOP Conference Series: Materials Science and Engineering, 2019, 557(1): 9.
[10] WANG Dong, SUN Jun-peng, YU Shi-feng, et al(王 冬, 孙俊鹏, 于世锋, 等). Journal of Food Safety & Quality(食品安全质量检测学报), 2021, 12(18): 7222.
[11] Paz P, Sanchez Maria-Teresa, Pérez-Marín D, et al. Journal of Agricultural and Food Chemistry, 2008, 56(8): 2565.
[12] Louw E D, Theron K I. Postharvest Biology and Technology, 2010, 58: 176.
[13] BAI Feng-hua, ZHANG Xiao-yu, WANG Yan-wei, et al(白凤华, 张晓瑜, 王艳伟, 等). Food Industry(食品工业), 2018, 39(6): 175.
[14] Workman J, Weyer J L. Practical Guide to Interpretive Near-Infrared Spectroscopy（近红外光谱解析实用指南）. Translated by CHU Xiao-li，XU Yu-peng, TIAN Gao-you（褚小立, 许育鹏, 田高友, 译）. Beijing: Chemical Industry Press(北京: 化学工业出版社), 2009. 19.
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