Identification of fig maturity based on near-infrared spectroscopy and partial least square-discriminant analysis

Authors

Jing-yu ZHOU, Qilu University of Technology, Shandong Academy of Science, College of Food Science andEngineering, Jinan, Shandong 250300 , China
Rui SUN, Qilu University of Technology, Shandong Academy of Science, College of Food Science andEngineering, Jinan, Shandong 250300 , China
Duo YU, Qilu University of Technology, Shandong Academyof Science, College of Mathematics and Statistics, Jinan, Shandong 250300 , China
Yu-xuan LV, Qilu University of Technology, Shandong Academy of Science, College of Food Science andEngineering, Jinan, Shandong 250300 , China
Yan-ling HAN, Qilu University of Technology, Shandong Academyof Science, College of Mathematics and Statistics, Jinan, Shandong 250300 , China

Abstract

In order to distinguish figs of different maturity accurately and quickly without damage, the samples of figs were collected by near-infrared spectroscopy, and K-means clustering was carried out for five indexes of fig sugar degree, single fruit weight, vertical diameter, horizontal diameter and hardness. According to the spectral data and the score chart of principal component analysis, the components of the optimal clustering effect and the index distribution of each category were determined. Based on the PLS-DA model, the cluster discrimination model was constructed to achieve the purpose of fruit maturity classification. Through the analysis of the differences of the above five indexes, significant differences in soluble solids, single fruit weight and hardness of the three kinds of fig samples at the maturity stage were found, and significant differences between the vertical and horizontal diameters of mature and growing fruits and young fruits were also detected. According to PLS-DA discriminant model, the classification accuracy of training set was 99.59%, and that of test set was 99.15%. The results showed that the PLS-DA model based on principal component analysis and spectral data had good performance, and it could be used to identify the maturity of figs quickly.

Publication Date

2-18-2023

First Page

107

Last Page

111

DOI

10.13652/j.issn.1003-5788.2020.11.021

Recommended Citation

ZHOU, Jing-yu; SUN, Rui; YU, Duo; LV, Yu-xuan; and HAN, Yan-ling (2023) "Identification of fig maturity based on near-infrared spectroscopy and partial least square-discriminant analysis," Food and Machinery: Vol. 36: Iss. 11, Article 21.
DOI: 10.13652/j.issn.1003-5788.2020.11.021
Available at: https://www.ifoodmm.cn/journal/vol36/iss11/21

References

[1] 孙锐,贾眀,孙蕾.世界无花果资源发展现状及应用研究[J].世界林业研究,2015,28(3):31-36.
[2] LADHARI A,GAALICHE B,ZARRELLI A,et al.Allelopathic potential and phenolic allelochemicals discrepancies in Ficus carica L.cultivars[J].South African Journal of Botany,2020,130:30-44.
[3] 柴金珍,黄远英,袁根良,等.无花果的药理作用研究进展[J].中成药,2016,38(8):1 805-1 810.
[4] 孙锐,孙蕾,贾明,等.山东引种无花果品种营养成分分析[J].经济林研究,2014,32(4):63-67.
[5] 廖亮,李瑾瑜,马红艳,等.贮藏温度和成熟度对新疆早黄无花果采后生理的影响[J].核农学报,2016,30(2):282-287.
[6] 孙锐,孙蕾,马金辉,等.各品种不同成熟度无花果质构特性分析[J].食品与机械,2017,33(2):22-25,30.
[7] ABDEL-ATY A M,HAMED M B,SALAMA W H,et al.Ficus carica,Ficus sycomorus and Euphorbia tirucalli latex extracts:Phytochemical screening,antioxidant and cytotoxic properties[J].Biocatalysis and Agricultural Biotechnology,2019,20:101199.
[8] PALMEIRA L,PEREIRA C,DIAS M I,et al.Nutritional,chemical and bioactive profiles of different parts of a Portuguese common fig(Ficus carica L.)variety[J].Food Research International,2019,126:108572.
[9] 潘悠优,花佩,王允祥,等.无花果多糖提取、分离纯化及生物活性的研究进展[J].食品科学,2016,37(17):289-295.
[10] 刘燕德,徐海,孙旭东,等.西红柿成熟度的近红外漫透射光谱无损检测[J].激光技术,2019,43(1):25-29.
[11] 牛晓颖,贡东军,王艳伟,等.基于近红外光谱和化学计量学的李果实成熟度鉴别方法研究[J].现代食品科技,2014,30(12):230-234,125.
[12] 孙锐,孙蕾,马金辉,等.不同成熟度无花果品质指标的变化分析[J].经济林研究,2017,35(2):32-37.
[13] RUNGPICHAYAPICHET P,MAHAYOTHEE B,NAGLE M,et al.Robust NIRS models for non-destructive prediction of postharvest fruit ripeness and quality in mango[J].Postharvest Biology and Technology,2016,111:31-40.
[14] POURDARBANI R,SABZI S,KALANTARI D,et al.Automatic non-destructive video estimation of maturation levels in Fuji apple(Malus Malus pumila)fruit in orchard based on colour(Vis)and spectral(NIR)data[J].Biosystems Engineering,2020,195:136-151.
[15] 李丽娜,赵武奇,曾祥源,等.苹果的质构与感官评定相关性研究[J].食品与机械,2017,33(6):37-41,45.
[16] 毕智健,张若宇,齐妍杰,等.基于机器视觉的番茄成熟度颜色判别[J].食品与机械,2016,32(12):133-136.
[17] 黄玉萍,刘英,杨雨图,等.空间分辨光谱和可见/近红外光谱的番茄颜色等级判别[J].光谱学与光谱分析,2019,39(11):3 585-3 591.
[18] 孙阳,白皓然,初留珠,等.基于Matlab的无花果成熟度识别技术[J].计算机测量与控制,2018,26(10):299-303.
[19] 章林忠,蔡雪珍,方从兵.近红外光谱定量和定性分析技术在鲜食葡萄果实无损检测中的应用[J].浙江农业学报,2018,30(2):330-338.
[20] CICCORITTI R,PALIOTTA M,AMORIELLO T,et al.FT-NIR spectroscopy and multivariate classification strategies for the postharvest quality of green-fleshed kiwifruit varieties[J].Scientia Horticulturae,2019,257:108622.
[21] WEI Xuan,HE Jin-cheng,ZHENG Shu-he,et al.Modeling for SSC and firmness detection of persimmon based on NIR hyperspectral imaging by sample partitioning and variables selection[J].Infrared Physics & Technology,2020,105:103099.