Abstract:The shoddy phenomena often occur in the market, and the producing year is an important index to measure the quality of dried tangerine. Thus, this research applied hyperspectral technique in the spectral windows of 380~1 023 and 874~1 734 nm combined with chemometric methods to identify different producing years of dried tangerine. Due to the actual detection of dried tangerine, spectra of front and back of dried tangerine were acquired. Hyperspectral imagesat both sides of a total of 180 samples of four years were collected within 380~1 023 and 874~1 734 nm (720 pictures). Then principal component analysis (PCA) was carried out on the spectral data, which had a qualitativeanalysis about the dried tangerine. Regression coefficient (RC) was chosen to select the sensitive variables. Partial least squares-discrimiant analysis (PLS-DA) were used to compare the performances of full-spectra and sensitive variables. Finally, the linear PLS-DA model and nonlinear ELM model were established based on the sensitive bands. The results demonstrated that most of the predictive effects in 874~1 734 nm were higher than that of 380~1 023 nm. ELM models were outperformed PLS-DA among all the developed models, the highest accuracy was achieved 100% in the model set, and 98.33% in the prediction set. No matter what kind of placement, the prediction accuracy rate was higher than 85%. Hence, hyperspectral technique with chemometrics models can realize nondestructive identification of various producing years of dried tangerine, which provides a theoretical reference and basis for developing instruments of recognition the dried tangerine in further research.
鲍一丹,吕阳阳,朱红艳,赵艳茹,何 勇. 陈皮年份的高光谱技术鉴别研究[J]. 光谱学与光谱分析, 2017, 37(06): 1866-1871.
BAO Yi-dan, LÜ Yang-yang, ZHU Hong-yan, ZHAO Yan-ru, HE Yong. Identification and Classification of Different Producing Years of Dried Tangerine Using Hyperspectral Technique with Chemometrics Models. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(06): 1866-1871.
[1] ZHANG Li-ping(张理平). The Bright Chinese Traditional Medicine(光明中医), 2005, 20(1): 41.
[2] HU Ji-teng, ZHAO Zhi-min, TANG Tie-xin, et al(胡继藤, 赵志敏, 唐铁鑫, 等). Chinese Journal of Experimental Traditional Medical Formulae(中国实验方剂学杂志), 2014, 20(9): 62.
[3] YIN Qing-juan(尹青娟). Journal Of Practical Medicine(实用中医药杂志), 2001, 17(6): 47.
[4] GUO Nian-xin, CAI Jia-liang, JI Sheng-guo, et al(郭念欣, 蔡佳良, 姬生国, 等). China Pharmacy(中国药房), 2013, 24(15): 1394.
[5] YANG Yi-ting, LUO Hu-jie, YE Yong-shu, et al(杨宜婷, 罗琥捷, 叶勇树, 等). Science and Technology of Food Industry(食品工业科技), 2011, 9: 258.
[6] HU Ji-teng, TANG Tie-xin, YANG Yi-ting, et al(胡继藤,唐铁鑫, 杨宜婷, 等). Lishizhen Medicine and Materia Medica Research(时珍国医国药), 2014, 25(7): 1646.
[7] Lorente D, Blasco J, Serrano A J, et al. Food & Bioprocess Technology, 2013, 6(12): 3613.
[8] Lorente D, Aleixos N, Gómez-Sanchis J, et al. Food and Bioprocess Technology, 2013, 6(2): 530.
[9] Yu Keqiang, Zhao Yanru, Li Xiaoli,et al. Computers and Electronics In Agriculture, 2014,(103): 1.
[10] ElMasry G, Sun D-W, Allen P. Food Research International, 2011, 44(9): 2624.
[11] Liu F, Jin Z L, Naeem M S, et al. Food and Bioprocess Technology, 2010, 4(7): 1314.
[12] Schrder S, Pavlov S G, Rauschenbach I, et al. Icarus, 2013, 223(1): 61.
[13] Luna A S, da Silva A P, Pinho J S, et al. Molecular and Biomolecular Spectroscopy,2013,(100): 115.
[14] Jiang H, Zhu W X. Food Analytical Methods, 2013, 6(2): 569.
[15] Abbott J A,Lu R. Upchurch B L, et al. Horticultural Review,1997,(20): 1.