Nondestructive Testing of Jujube Water Based on the NTRS

doi:10.6048/j.issn.1001-4330.2021.12.019

Abstract

Abstract:

【Objective】 Which lays a foundation for the establishment of the water content model of jujube in the next step.【Methods】 In this experiment, the fruits of Manman jujube and Baodeyou jujube were used as test materials. The water content of jujube was determined by traditional drying method and the moisture content was detected by NIR. 【Results】 The spectra of two varieties were divided into sample sets, and the water content quantitative analysis model was established by using savitzky-Golay smoothing method and partial least square regression analysis method (PLS). A total of 212 samples were obtained, of which 100 and 112 were Manman jujube and Baode jujube respectively, 75 and 84 were random correction models of the two varieties, 25 and 28 were verification models, respectively. The experimental group was divided into two parts: red jujube water content correction model and validation model. The values of SEC (standard deviation of calibration set) and SEP (standard deviation of prediction set) were 1.01% and 1.29% respectively, and the correlation coefficients between calibration set and validation set were 0.878 and 0.883, respectively. 【Conclusion】 S-G smoothing method and PLS regression analysis method are used to establish a water content quantitative detection and analysis model for jujube. The accuracy of water content detection is high, and the cross-correlation coefficient between the real value and the predicted value of water content is higher than 0.850. The difference between the two models is about 0.5%. By analyzing the characteristics of the spectrum, the relationship between the near-infrared spectrum and some quality of jujube is preliminarily established.

Key words: near infrared spectrum; jujube; water content; nondestructive testing

摘要： 目的基于近红外光谱技术的红枣水分无损检测,为红枣水分含量模型建立提供科学依据。方法以塔里木大学园艺试验站红枣资源圃中的脆熟期馒馒枣和保德油枣的果实为试材,采用传统烘干法测定枣果实水分含量,并通过近红外光谱分析仪进行枣水分无损检测。对2个品种样本光谱进行样本集划分并使用预处理的方法Savitzky-Golay平滑法和偏最小二乘回归分析法(PLS)。结果建立了含水量定量检测分析模型。共获得212个样本,馒馒枣和保德油枣分别为100和112个,2个品种随机校正模型为75和84个,验证模型分别为25和28个,用外部证实法建立样品校正模型和验证模型。建立光谱模型将试验组分别分为红枣含水量校正模型和验证模型。所建2种红枣水分检测模型中SEC(校正集标准偏差)值分别为1.01%和1.29%;SEP(预测标准偏差)值为1.65%和1.41%,2种红枣的校正集与验证集交互相关系数分别为0.878和0.883。结论以S-G平滑法对光谱数据预处理,以偏最小二乘进行回归分析(PLS)。建立含水量定量检测分析模型对红枣进行水分检测,水分真实值和预测值的交互相关系数均高于0.850。2个品种校正模型和验证模型差异较小均在0.5%左右,建立了红枣近红外光谱和水分含量之间的对应关系。

关键词: 近红外光谱, 红枣, 含水量, 无损检测

CLC Number:

S226

YANG Zhi, WANG Zhenlei, LIN Minjuan. Nondestructive Testing of Jujube Water Based on the NTRS[J]. Xinjiang Agricultural Sciences, 2021, 58(12): 2320-2326.

杨植, 王振磊, 林敏娟. 基于近红外光谱技术的红枣水分无损检测[J]. 新疆农业科学, 2021, 58(12): 2320-2326.

Figures/Tables 8

References 26

[1]	柳维扬, 蒋学玮, 曹琦, 等. 盐胁迫对南疆滴灌枣园红枣生长的影响[J]. 北方园艺, 2018,(1):67-71.
	LIU Weiyang, JIANG Xuewei, CAO Qi, et al. Effects of Salt Stress on Jujube Growth in Drip Irrigation Yard in Southern Xinjiang[J]. Northern Horticulture, 2018,(1):67-71.
[2]	刘孟军. 枣属植物分类学研究进展-文献综述[J]. 园艺学报, 1999,(5):302-308.
	LIU Mengjun. Advances in Taxonomy Study on the Genus Ziziphus[J]. Acta Horticulturae Sinica, 1999,26(5):302-308.
[3]	彭云发, 彭海根, 詹映, 等. 近红外光谱对南疆红枣水分无损检测的研究[J]. 食品科技, 2013,38(11):260-263.
	PENG Yunfa, PENG Haigeng, ZHAN Ying, et al. Nondestructive testing of jujube water in south Xinjiang by NIRS[J]. Food Science and Technology, 2013,38(11):260-263.
[4]	褚小立, 史云颖, 陈瀑, 等. 近五年我国近红外光谱分析技术研究与应用进展[J]. 分析测试学报, 2019,38(5):603-611.
	CHU Xiaoli, SHI Yunying, CHEN Pu, et al. Research and Application Progresses of Near Infrared Spectroscopy Analytical Technique in China in Past Five Years[J]. Journal of Instrumental Analysis, 2019,38(5):603-611.
[5]	李江波, 饶秀勤, 应义斌. 农产品外部品质无损检测中高光谱成像技术的应用研究进展[J]. 光谱学与光谱分析, 2011,31(8):2021-2026.
	LI Jiangbo, RAO Xiuqin, YING Yibin. Advance on Application of Hyperspectral Imaging to Nondestructive Detection of Agricultural Products External Quality[J]. Spectroscopy and Spectral Analysis, 2011,31(8):2021-2026.
[6]	李金梦, 叶旭君, 王巧男, 等. 高光谱成像技术的柑橘植株叶片含氮量预测模型[J]. 光谱学与光谱分析, 2014,34(1):212-216.
	LI Jinmeng, YE Xujun, WANG Qiaonan, et al. Development of Prediction Models for Determining N Content in Citrus Leaves Based on Hyperspectral Imaging Technology[J]. Spectroscopy and Spectral Analysis, 2014,34(1):212-216.
[7]	庄新港. 近红外光谱分析应用研究及新型光谱感知节点入射光学系统设计[D]. 济南:山东大学, 2017.
	ZHUANG Xingang. Applied study of near-infrared Spectroscopy and Indidence Optical System Design of New SPectral Sensing Node[D]. Jinan: Shangdong University, 2017.
[8]	彭海根, 彭云发, 詹映, 等. 近红外光谱技术结合联合区间间隔偏最小二乘法对南疆红枣糖度的测定[J]. 食品科技, 2014,39(6):276-280.
	PENG Haigeng, PENG Yunfa, ZHAN Yin, et al. Determination of the sugar content of jujube in south Xinjiang by near infrared spectroscopy combined with siPLS methods[J]. Food Science and Technology, 2014,39(6):276-280.
[9]	罗华平, 卢启鹏. 近红外拓扑方法在南疆红枣品质分析中的应用[J]. 光谱学与光谱分析, 2012,32(3):655-659.
	LUO Huaping, LU Qipeng. Application of Near-Infrared Topology Method in the Quality Analysis of Jujube of Southern Xinjiang[J]. Spectroscopy and Spectral Analysis, 2012,32(3):655-659.
[10]	李光君. 热成像技术与近红外光谱技术结合无损检测西拉葡萄叶片水分含量[J]. 山西农业科学, 2016,44(10):1467-1475.
	LI Guangjun. Combination of Thermal Imaging and NIR Spectroscopy Technology to Test Water Content in Shiraz Leaves[J]. Journal of Shanxi Agricultural Sciences, 2016,44(10):1467-1475.
[11]	Nicolaï B M, Beullens K, Bobelyn E. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review[J]. Postharvest Biology & Technology, 2007,46(2):99-118.
[12]	Barnes R J, Dhanoa S M, Lister J S. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra[J]. Applied Spectroscopy, 2016,43(5):772-777.
[13]	Gorry A P. General least-squares smoothing and differentiation by the convolution (Savitzky-Golay) method[J]. Analytical Chemistry, 1990,62(6):570-573.
[14]	Delwiche S R, Mekwatanakarn W, Wang Y C. Soluble solids and simple measurement in intact mango using near infrared spectroscopy[J]. Horttechnology, 2008,(18).
[15]	Nicolaï B M, Verlinden B E, Desmet M. Time-resolve and continuous wave NIR reflectance spectroscopy to predict soluble solids content and firmness of pear[J]. Postharvest Biology & Technology, 2008,47(1):68-74.
[16]	Travers S, Bertelsen M G, Petersen K K. Predicting pear (cv. Clara Frijs) dry matter and soluble solids content with near infrared spectroscopy[J]. Lebensmittel-Wissenschaft und-Technologie, 2014,59(2):1107-1113.
[17]	Ito S, Ootake Y, Yoshimura Y E A. Nondestructive determination of sugar content in watermelon by near infrared spectroscopy[J]. Research Bulletin of the Aichi-ken Agricultural Research Center, 1993,31:153-158.
[18]	Ito H, Fukino-Ito N, Horie H E A. Non-destructive detection of physiological disorders in melons using near infrared spectroscopy[J]. Acta Horticulture, 2003,645:229-234.
[19]	Guthrie J, Wedding B. Robustness of NIR calibrations for soluble solids in intact melon and pineapple[J]. Journal of Near Infrared Spectroscopy, 1998,6:259-265.
[20]	Guthrie J A, Liebenberg C J, Walsh K B. NIR model development and robustness in prediction of melon fruit total soluble solids[J]. Australian Journal of Agricultural Research, 2006,57(4):207-213.
[21]	Jha S N, R G. Non-destructive prediction of quality of intact apple using near infrared spectroscopy[J]. Journal of Food Science & Technology, 2010,47(2):207-213.
[22]	李桂峰, 赵国建, 刘兴华, 等. 苹果硬度的傅里叶变换近红外光谱无损检测[J]. 农业机械学报, 2009,40(1):120-123.
	LI Guifeng, ZHAO Guojian, LIU Xinghua, et al. Using FT-NIR Spectra in Non-invasive Measurements of Apple Firmness[J]. Transactions of the Chinese Society for Agricultural Machinery, 2009,40(1):120-123.
[23]	屠振华, 籍保平, 孟超英, 等. 基于遗传算法和间隔偏最小二乘的苹果硬度特征波长分析研究[J]. 光谱学与光谱分析, 2009,29(10):2760-2764.
	TU Zhenghua, JI Baoping, MENG Chaoying, et al. Analysis of NIR Characteristic Wavelengths for Apple Flesh Firmness Based on GA and iPLS[J]. Spectroscopy and Spectral Analysis, 2009,29(10):2760-2764.
[24]	王加华, 汤智辉, 韩东海. 多年份苹果糖度近红外预测模型建立[J]. 食品安全质量检测学报, 2014,5(3):742-747.
	WANG Jiahua, TANG Zhihui, HAN Donghai. Developing a predictive model of soluble solids content for apple harvested at different years[J]. Journal of Food Safety & Quality, 2014,5(3):742-747.
[25]	毛莎莎, 曾明, 何绍兰, 等. 哈姆林甜橙果实内在品质的可见-近红外漫反射光谱无损检测法[J]. 食品科学, 2010,31(14):258-263.
	MAO Shasha, ZENG Ming, HE Shaolan. Non-destructive Measurement of Soluble Solids, Vitamin C and Titratable Acidity of Hamlin Sweet Orange using Vis/NIR Spectrometry[J]. Food Science, 2010,31(14):258-263.
[26]	刘洁. 基于近红外光谱技术的板栗品质无损检测方法研究[D]. 武汉:华中农业大学, 2011.
	LIU Jie. Nondestructive measurement of properties of Chestnut based on near infrared spectroscopy[D]. Wuhan: Huazhong Agricultural University, 2011.

品种 Varie ties	样品集 Sample set	样品数 Number of samples	均值 Mean (%)	最大值 Max (%)	最小值 Min (%)	标准差 Standard deviation (%)
馒馒枣 Manm anzao	正集	75	72.14	76.23	66.06	2.11
馒馒枣 Manm anzao	验证集	25	71.79	75.08	66.51	2.16
保德油枣 Baode youzao	校正集	84	65.26	72.10	58.74	2.73
保德油枣 Baode youzao	验证集	28	64.92	71.72	59.81	2.96

品种 Varie ties	样品集 Sample set	样品数 Number of samples	均值 Mean (%)	最大值 Max (%)	最小值 Min (%)	标准差 Standard deviation (%)
馒馒枣 Manm anzao	正集	75	72.14	76.23	66.06	2.11
馒馒枣 Manm anzao	验证集	25	71.79	75.08	66.51	2.16
保德油枣 Baode youzao	校正集	84	65.26	72.10	58.74	2.73
保德油枣 Baode youzao	验证集	28	64.92	71.72	59.81	2.96

馒馒枣 Manmanzao				保德油枣 Baodeyouzao
编号 No	预测值 Predicted value (%)	真实值 True value (%)	偏差 Deviation	编号 No	预测值 Predicted value (%)	真实值 True value (%)	偏差 Deviation
1A	72.94	73.14	-0.20	1A	65.74	69.05	-3.31
2A	72.79	74.67	-1.88	2A	68.89	66.96	1.93
3A	73.82	72.59	1.23	3A	66.92	68.00	-1.08
4A	69.78	71.88	-2.10	4A	68.55	71.72	-3.17
5A	70.69	71.22	-0.53	5A	65.78	65.44	0.34
6A	71.44	68.78	2.66	6A	64.28	64.75	-0.47
7A	70.51	66.51	4.00	7A	67.90	70.97	-3.07
8A	72.40	70.85	1.55	8A	64.98	64.65	0.33
9A	69.65	68.46	1.19	9A	63.58	63.38	0.20
10A	73.33	72.71	0.62	10A	64.04	63.93	0.11
11A	76.09	72.88	3.21	11A	65.38	65.19	0.19
12A	70.00	70.00	0.00	12A	59.62	60.25	-0.63
13A	73.73	74.82	-1.09	13A	62.93	65.32	-2.39
14A	73.09	72.15	0.94	14A	63.93	64.79	-0.86
15A	71.71	70.64	1.07	15A	64.61	63.85	0.76
16A	73.4	73.51	-0.11	16A	66.72	67.47	-0.75
17A	72.24	73.74	-1.50	17A	61.33	61.29	0.04
18A	74.76	73.39	1.37	18A	63.23	62.35	0.88
19A	71.3	70.16	1.14	19A	60.62	59.81	0.81
20A	69.94	67.97	1.97	20A	67.28	67.97	-0.69
21A	75.89	75.08	0.81	21A	61.53	62.42	-0.89
22A	71.37	70.65	0.72	22A	65.22	64.71	0.51
23A	73.03	74.19	-1.16	23A	66.65	63.85	2.80
24A	71.06	72.25	-1.19	24A	67.69	70.59	-2.9
25A	72.96	72.3	0.66	25A	63.97	64.29	-0.32
				26A	61.42	62.50	-1.08
				27A	64.42	65.63	-1.21
				28A	67.71	65.33	2.38

馒馒枣 Manmanzao				保德油枣 Baodeyouzao
编号 No	预测值 Predicted value (%)	真实值 True value (%)	偏差 Deviation	编号 No	预测值 Predicted value (%)	真实值 True value (%)	偏差 Deviation
1A	72.94	73.14	-0.20	1A	65.74	69.05	-3.31
2A	72.79	74.67	-1.88	2A	68.89	66.96	1.93
3A	73.82	72.59	1.23	3A	66.92	68.00	-1.08
4A	69.78	71.88	-2.10	4A	68.55	71.72	-3.17
5A	70.69	71.22	-0.53	5A	65.78	65.44	0.34
6A	71.44	68.78	2.66	6A	64.28	64.75	-0.47
7A	70.51	66.51	4.00	7A	67.90	70.97	-3.07
8A	72.40	70.85	1.55	8A	64.98	64.65	0.33
9A	69.65	68.46	1.19	9A	63.58	63.38	0.20
10A	73.33	72.71	0.62	10A	64.04	63.93	0.11
11A	76.09	72.88	3.21	11A	65.38	65.19	0.19
12A	70.00	70.00	0.00	12A	59.62	60.25	-0.63
13A	73.73	74.82	-1.09	13A	62.93	65.32	-2.39
14A	73.09	72.15	0.94	14A	63.93	64.79	-0.86
15A	71.71	70.64	1.07	15A	64.61	63.85	0.76
16A	73.4	73.51	-0.11	16A	66.72	67.47	-0.75
17A	72.24	73.74	-1.50	17A	61.33	61.29	0.04
18A	74.76	73.39	1.37	18A	63.23	62.35	0.88
19A	71.3	70.16	1.14	19A	60.62	59.81	0.81
20A	69.94	67.97	1.97	20A	67.28	67.97	-0.69
21A	75.89	75.08	0.81	21A	61.53	62.42	-0.89
22A	71.37	70.65	0.72	22A	65.22	64.71	0.51
23A	73.03	74.19	-1.16	23A	66.65	63.85	2.80
24A	71.06	72.25	-1.19	24A	67.69	70.59	-2.9
25A	72.96	72.3	0.66	25A	63.97	64.29	-0.32
				26A	61.42	62.50	-1.08
				27A	64.42	65.63	-1.21
				28A	67.71	65.33	2.38