基于可见−近红外光谱技术的果蔬品质检测方法

韩亚芬; 吴尘萱; 吴海华; 吕程序; 何亚凯; 杨葆华; 苑严伟

doi:10.19998/j.cnki.2095-1795.2024.01.017

基于可见−近红外光谱技术的果蔬品质检测方法

doi: 10.19998/j.cnki.2095-1795.2024.01.017

韩亚芬^{1, 2,},
吴尘萱^{1, 2},
吴海华^{1, 2},
吕程序^{1, 2},
何亚凯^{2, 3},
杨葆华^{1, 2},
苑严伟^{1, 2,}

1.
中国农业机械化科学研究院集团有限公司，北京 100083
2.
农业装备技术全国重点实验室，北京 100083
3.
中国包装和食品机械有限公司，北京 100083

基金项目: 国家重点研发计划项目（2022YFD2002205）；北京市科学技术协会青年人才托举工程项目（BYESS2023431）

详细信息

作者简介:
韩亚芬，博士，高级工程师，主要从事智能检测技术研究　E-mail：hyf0825@126.com

苑严伟，通信作者，博士，研究员，主要从事智能检测技术研究　E-mail：yyw215@163.com

中图分类号: S126
计量
- 文章访问数: 36
- HTML全文浏览量: 12
- PDF下载量: 12
- 被引次数: 0
出版历程
- 收稿日期: 2023-11-12
- 修回日期: 2023-12-11
- 出版日期: 2024-01-20

Detection Method of Fruit and Vegetable Quality Based on VIS-NIR Spectroscopy

HAN Yafen^{1, 2
,},
WU Chenxuan^{1, 2},
WU Haihua^{1, 2},
LV Chengxu^{1, 2},
HE Yakai^{2, 3},
YANG Baohua^{1, 2},
YUAN Yanwei^{1, 2
,}

1.
Chinese Academy of Agricultural Mechanization Sciences Group Co.，Ltd.，Beijing 100083，China
2.
State Key Laboratory of Agricultural Equipment Technology，Beijing 100083，China
3.
China Packaging and Food Machinery Co.，Ltd.，Beijing 100083，China

摘要

摘要:
可见−近红外光谱技术利用波长在380～2500 nm的电磁波获取果蔬中有机分子含氢基团的特征信息，根据样品对不同波长光的吸收信息，实现果蔬的外部、内部缺陷及营养成分定性、定量分析，是目前主流的果蔬内外部品质快速无损检测技术。综述了目前基于吸光度谱和能量谱对果蔬营养物质含量定量分析及缺陷定性分析，所使用的检测模型和变量筛选模型及其检测准确性，为相关研究人员选择高效准确的检测模型提供技术支撑。
- 可见−近红外光谱 /
- 果蔬品质检测 /
- 能量谱 /
- 吸光度谱 /
- 变量优化
Abstract:
Visible near-infrared（VIS-NIR）spectroscopy technology is currently mainstream rapid non-destructive testing technology for internal and external quality of fruits and vegetables．Electromagnetic waves in wavelength range of 380 to 2500 nm are used to obtain characteristics of hydrogen-containing groups of organic molecules in fruits and vegetables．Based on absorption information of samples to different wavelengths of light, qualitative and quantitative analysis of external and internal defects and nutrient contents of fruits and vegetables are achieved．Latest detection models, variable screening models, and their detection accuracy used in quantitative analysis and qualitative defect analysis of fruit and vegetable nutrient content based on absorbance spectrum and energy spectrum were reviewed, providing technical support for relevant scientists to select efficient and accurate detection models．
- VIS-NIR spectroscopy /
- quality detection of fruits and vegetables /
- energy spectrum /
- absorbance spectrum /
- variable optimization

HTML全文

表 1 果蔬品质定量预测模型

Table 1. Quantitative prediction model of fruit and vegetable quality

检测指标	定量模型	预测准确性
草莓SSC^[3]	MLR	E_SEP：0.21°Brix
葡萄还原糖^[4]	MLR	E_SEP：19.97 g/L
马铃薯直链淀粉^[5]	MLR	E_SECV：0.89%
赣南脐橙SSC^[6]	PLSR	E_RMSEP：0.46°Brix
芒果SSC^[7]	PLSR	E_SEP：3.44°Brix
苹果、香蕉、马铃薯和洋葱等15种果蔬SSC^[8]	PLSR	E_RMSECV：0.22～1.92°Brix
猕猴桃、芒果、西红柿和马铃薯等7种果蔬DM^[8]	PLSR	E_RMSECV：0.20%～1.54%
赣南脐橙SSC^[1]	LSSVR	E_RMSEP：0.32°Brix
赣南脐橙SSC^[2]	ANN	E_RMSEP：0.70°Brix
注：预测准确性为最优模型条件下模型对验证集的预测标准误差E_SEP或交互验证、预测均方根误差E_RMSECV、E_RMSEP。

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表 2 基于能量谱的水果缺陷定性判别模型

Table 2. Qualitative identification model of fruit defect based on energy spectrum

检测指标	判别模型	预测准确性/%
马铃薯黑心病^[15]	（T₆₉₈−T₆₅₇）/T₆₂₄	93.69
富士苹果水心病、内部腐败^[16]	水心病\|T₆₄₅−T₇₁₀\|/T₆₇₅ 内部腐败\|T₇₁₀−T₈₀₀\|/T₆₇₅	正常果100、水心果91、内部腐败果98
苹果内部褐变^[17]	710～900 nm峰面积	98
鸭梨黑心病^[18]	T₆₈₁/T₈₂₂	98
鸭梨黑心病^[12]	PLS-DA	健康梨97、黑心梨100
苹果霉心病等^[13]	SVM	97
苹果霉心病^[14]	DBN	88
注：预测准确性为最优模型条件下模型对验证集的判别正确率。

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表 3 基于吸光度谱的果蔬缺陷定性判别模型

Table 3. Qualitative identification model of fruit and vegetable defect based on absorbance spectrum

检测指标	判别模型	预测准确性/%
苹果损伤^[19]	PLS-DA	100
番茄碰伤^[20]	PLS-DA	100
马铃薯碰伤^[21]	PLS-DA	93
苹果内部褐变^[22]	PLS-DA	＞95
板栗褐变^[23]	LSSVM	95
苹果腐心病^[24]	LDA	完好95、轻度81、中度87、严重95
缺陷枣^[25]	SIMCA	完好96、挤压损伤96、擦伤94、虫洞96
鲜枣裂果^[26]	MLP-ANN	100
李果实褐变^[27]	BP-ANN	98
注：预测准确性为最优模型条件下模型对验证集的判别正确率。

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表 4 果蔬品质定量、定性预测模型变量优化研究

Table 4. Variable optimization of quantitative and qualitative prediction model for fruit and vegetable quality

检测指标	变量优化算法	原变量数	优选变量数	全变量预测准确性	优化后预测准确性
西瓜可溶性固形物含量^[37]	UVE-SMLR	314	13	0.30°Brix	0.33°Brix
猕猴桃损伤	SPA-LSSVM	/	5	96%	98%
库勒尔香梨硬度	SPA-PLS	501	32	0.56 N	0.49 N
库勒尔香梨可溶性固形物含量	CARS-PLS	501	24	0.39°Brix	0.37°Brix
马铃薯淀粉	CARS-PLS	/	22	0.79%	0.63%
苹果可溶性固形物含量	BiPLS	701	350	0.30°Brix	0.28°Brix
葡萄可溶性固形物含量	GA-LSSVM	/	5	0.93°Brix	0.96°Brix
红提维生素C含量	CARS-SPA-PLS	1557	59	5.85%	2.00%
西红柿番茄红素	UVE-CARS-PLS	942	67	1.06 mg/kg	0.71 mg/kg
苹果可溶性固形物含量	MW-GA-PLS	771	36	0.74°Brix	0.70°Brix
草莓可溶性固形物含量	BiPLS-SA-MLR	3112	7	0.55°Brix	0.43°Brix
苹果、梨、桃可溶性固形物含量通用模型^[15]	MWPLS-SPA-MLR	/	3	0.89°Brix	0.46°Brix
注：原变量数为初始模型全变量数；优选变量数为最优条件下建模变量数；预测准确性为最优条件下模型E_RMSEP或判别正确率。

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