Effect of Pericarp on Prediction Accuracy of Soluble Solid Content in Navel Oranges by Visible/Near Infrared Spectroscopy
SUN Tong, MO Xin-xin, LIU Mu-hua*
Key Laboratory of Jiangxi University for Optics-Electronics Application of Biomaterials, College of Engineering, Jiangxi Agricultural University; Collaborative Innovation Center of Postharvest Key Technology and Quality Safety of Fruits and Vegetables in Jiangxi Province, Nanchang 330045，China
Abstract：Visible/near infrared (Vis/NIR) spectroscopy was used to determine soluble solid content (SSC) of navel oranges with pericarp and without pericarp, and the effect of pericarp on prediction accuracy of SSC of navel oranges was investigated. In addition, Vis/NIR spectra of navel oranges with pericarp and without pericarp were acquired by a QualitySpec spectrometer in the wavelength range of 350~1 000 nm, and the effect of pericarp was analyzed from two aspects of spectrum and model performance. The average spectra of navel oranges with pericarp and without pericarp were compared, and 20 principal components that obtained were used for multivariate analysis of variance (MANOVA). Moreover, partial least squares (PLS) regression combined with different pretreatment methods was used to develop calibration models of SSC for navel oranges with pericarp and without pericarp. Furthermore, the performance of models was compared, and square of prediction residuals of samples in prediction set were used for analysis of variance (ANOVA). The results indicate that the effect of pericarp on prediction accuracy of soluble solid content in navel oranges is significant at 5% confidence level. The correlation coefficients of prediction set and root mean square errors of prediction (RMSEPs) of PLS of SSC for navel oranges with pericarp and without pericarp are 0.888, 0.456% and 0.944, 0.324%, respectively.
孙 通，莫欣欣，刘木华. 果皮对脐橙可溶性固形物可见/近红外检测精度的影响[J]. 光谱学与光谱分析, 2018, 38(05): 1406-1411.
SUN Tong, MO Xin-xin, LIU Mu-hua. Effect of Pericarp on Prediction Accuracy of Soluble Solid Content in Navel Oranges by Visible/Near Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1406-1411.
[1] Fan S, Guo Z, Zhang B, et al. Food Analytical Methods, 2016, 9(5): 1333.
[2] Fan S, Zhang B, Li J, et al. Biosystems Engineering, 2016, 143(45): 9.
[3] Sun X, Liu Y, Li Y, et al. Postharvest Biology & Technology, 2016, 116: 80.
[4] Fang L, Li H, Liu Z, et al. Advance Journal of Food Science & Technology, 2013, 5(5): 606.
[5] Wang A, Xie L. Journal of Food Engineering, 2014, 143: 17.
[6] Marques E J, de Freitas S T, Pimentel M F, et al. Food Chemistry, 2016, 197: 1207.
[7] Santagapita P R, Tylewicz U, Panarese V, et al. Biosystems Engineering, 2016, 142: 101.
[8] Giovenzana V, Civelli R, Beghin R, et al. Talanta, 2015, 144: 584.
[9] SHI Ji-yong, YIN Xiao-ping, ZOU Xiao-bo, et al(石吉勇, 殷晓平, 邹小波, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2010, 41(9): 99.
[10] Lu H, Jiang H, Fu X, et al. Transactions of the ASABE, 2008, 51(3): 1009.
[11] Liu Y, Gao R, Hao Y, et al. Food and Bioprocess Technology, 2012, 5(3): 1106.
[12] XU Wen-li, SUN Tong, WU Weng-qiang, et al(许文丽，孙 通，吴文强, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(11): 3002.
[13] XU Wen-li, SUN Tong, WU Wen-qiang, et al(许文丽, 孙 通, 吴文强, 等). Acta Photonica Sinica(光子学报), 2013, 42(12): 1487.
[14] Liu C, Yang S X, Deng L. Expert Systems with Applications, 2015, 42(22): 8497.
[15] Liu C, Yang S X, Deng L. Journal of Food Engineering, 2015, 161: 16.
[16] SUN Tong, LIN Jin-long, XU Wen-li, et al(孙 通, 林金龙, 许文丽, 等). Journal of Jiangsu University·Natural Science Edition(江苏大学学报·自然科学版), 2013, 34(6): 663.
[17] HU Run-wen, XIA Jun-fang(胡润文, 夏俊芳). Food Science(食品科学), 2012, 33(3): 28.
[18] Liu Y, Sun X, Ouyang A. LWT- Food Science and Technology, 2010, 43(4): 602.
[19] XUE Long, LI Jing, LIU Mu-hua, et al(薛 龙, 黎 静, 刘木华, 等). Laser & Optoelectronics Progress (激光与光电子学进展), 2010, 47(12): 109.
