基于近红外光谱技术的红枣水分无损检测

doi:10.6048/j.issn.1001-4330.2021.12.019

新疆农业科学 ›› 2021, Vol. 58 ›› Issue (12): 2320-2326.DOI: 10.6048/j.issn.1001-4330.2021.12.019

• 植物保护·园艺特产·土壤肥料·节水灌溉·农业生态环境·农业装备工程与机械化 • 上一篇下一篇

基于近红外光谱技术的红枣水分无损检测

杨植(), 王振磊, 林敏娟()

南疆特色果树高效优质栽培与深加工技术国家地方联合工程实验室/新疆生产建设兵团南疆特色果树生产工程实验室/ 塔里木大学植物科学学院/新疆生产建设兵团塔里木盆地生物资源保护利用重点实验室,新疆阿拉尔 843300

收稿日期:2020-10-01 出版日期:2021-12-20 发布日期:2021-12-31
通讯作者: 林敏娟
作者简介:杨植(1995-),男,硕士研究生,研究方向为,(E-mail) 1256007929@qq.com
基金资助:
兵团中青年科技创新领军人才计划项目(2018CB032);兵团重大科技项目(2013AA001-1)

Nondestructive Testing of Jujube Water Based on the NTRS

YANG Zhi(), WANG Zhenlei, LIN Minjuan()

The National and Local Joint Engineering Laboratory of High Efficiency and Superior-Quality Cultivation and Fruit Deep Processing Technology of Characteristic Fruit Trees in Southern Xinjiang, College of Plant Science, Tarim University, Key Laboratory of Protection and Utilization of Biological Resources in Tarim Basin, Xinjiang Production & Construction Crops, Tarim University, Alar Xinjiang 843300, China

Received:2020-10-01 Online:2021-12-20 Published:2021-12-31
Contact: LIN Minjuan
Supported by:
Young and Middle-Aged Science and Technology Innovation Leadership Talent Project of XPCC(2018CB032);Major R & D Project of XPCC(2013AA001-1)

摘要/Abstract

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

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

中图分类号:

S226

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.

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

图/表 8

参考文献 26

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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

基于近红外光谱技术的红枣水分无损检测

Nondestructive Testing of Jujube Water Based on the NTRS

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