Prediction of multi-source fusion of β-carotene during pumpkin drying

doi:10.3969/j.issn.1004-1524.20221004

Acta Agriculturae Zhejiangensis ›› 2023, Vol. 35 ›› Issue (8): 1876-1887.DOI: 10.3969/j.issn.1004-1524.20221004

• Food Science • Previous Articles Next Articles

Prediction of multi-source fusion of β-carotene during pumpkin drying

NING Wenkai¹^,²(), LI Jing¹^,², SHEN Xiaodong¹^,², WU Xin¹, LI Zhenfeng¹^,²^,^*()

1. School of Mechanical Engineering, Jiangnan University, Wuxi 214122, Jiangsu, China
2. Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Wuxi 214122, Jiangsu, China

Received:2022-07-06 Online:2023-08-25 Published:2023-08-29

Abstract

Abstract:

β-carotene is an important index of pumpkin quality. The traditional method has a long period and a complicated process. To achieve the prediction of β-carotene content in pumpkin in the process of microwave drying, this study built a microwave drying system containing moisture content detection unit, machine vision and electronic nose units, the moisture content and appearance shape (surface shrinkage rate, and color difference) and odor characteristics of pumpkin under different temperatures of 60, 70, 80 ℃ in the drying process were on-line detected, meanwhile, the content of β-carotene was detected. The results showed that there was a significant correlation between the content of β-carotene and the appearance and flavor characteristics of pumpkin during drying. By establishing a monophyletic (machine vision or electronic nose) and multi-source fusion extreme learning machine (machine vision fusion electronic nose) model, the β-carotene content of pumpkin in the process of drying was predicted, and the results showed that the monophyletic model in machine vision had better prediction effect than electronic nose model, and the prediction accuracy of multi-source fusion model was the highest, $R p 2$ >0.97. The extreme learning machine model based on multi-source fusion can effectively predict the content of β-carotene in microwave drying of pumpkin, which has a good application prospect in automatic processing and quality monitoring of pumpkin.

Key words: pumpkin, machine vision, electronic nose, β-carotene, prediction

CLC Number:

TS207.3

NING Wenkai, LI Jing, SHEN Xiaodong, WU Xin, LI Zhenfeng. Prediction of multi-source fusion of β-carotene during pumpkin drying[J]. Acta Agriculturae Zhejiangensis, 2023, 35(8): 1876-1887.

Figures/Tables 17

Fig.1 Schematic diagram of the test system

Table 1 PEN3 electronic nose sensor and its performance description

传感器 Sensor	传感器型号 Sensor type	敏感气体类型 Sensitive gas type
1	W1C	对芳香物族化合物灵敏Sensitive to aromatic compounds
2	W5S	对含氮氧类物质灵敏Sensitive to nitrogen and oxygen containing substances
3	W3C	对氨等芳香物族化合物灵敏Sensitive to aromatic compounds such as ammonia
4	W6S	对氢气灵敏Sensitive to hydrogen gas
5	W5C	对烷烃、芳香族化合物灵敏Sensitive to alkanes and aromatic compounds
6	W1S	对甲基类物质灵敏Sensitive to methyl substances
7	W1W	对有机硫化物灵敏Sensitive to organic sulfides
8	W2S	对含有芳香族化合物的醇类灵敏Sensitive to alcohols containing aromatic compounds
9	W2W	对芳香成分灵敏Sensitive to aromatic components
10	W3S	对高浓度烷烃-脂肪族物质灵敏Sensitive to high concentrations of alkanes and aliphatic substances

Fig.2 Effects of different drying temperatures on drying characteristics of pumpkin

Fig.3 Effects of different drying temperatures on β-carotene content of pumpkin

Fig.4 Collected images of pumpkin appearance at different drying temperatures

Fig.5 Effect of different drying temperatures on surface shrinkage of pumpkin a, Longitudinal section; b, Cross section.

Fig.6 Influence of different drying temperatures on pumpkin color difference

Fig.7 Pumpkin fresh sample electronic nose sensor radar

Fig.8 Changes in response values of each sensor during pumpkin drying

Fig.9 Influence of different drying temperatures on response value of electronic nose sensor a, Sensor 2; b, Sensor 6; c, Sensor 7; d, Sensor 9.

Fig.10 Kinetic fitting of β-carotene a; First-order dynamics; b, Second-order dynamics; c, Third-order dynamics.

Table 2 Kinetic correlation coefficient of β-carotene

温度 Temperature/ ℃	一级动力学First-order dynamics			二级动力学Second-order dynamics			三级动力学Third-order dynamics
温度 Temperature/ ℃	k/min^-1	R²	RMSE	k/(mg· kg^-1)^-1·min^-1	R²	RMSE	k/(mg· kg^-1)^-2·min^-1	R²	RMSE
60	0.005 83	0.858 74	0.020 29	2.55×10^-4	0.901 23	2.39×10^-5	1.189×10^-5	0.940 91	2.975×10^-8
70	0.006 69	0.891 46	0.010 33	2.48×10^-4	0.917 25	1.049×10^-5	9.533×10^-6	0.932 85	1.240×10^-8
80	0.011 73	0.957 69	0.006 27	5.15×10^-4	0.985 39	4.060×10^-6	2.409×10^-6	0.980 99	1.160×10^-8

Table 3 Correlation between β-carotene content and characteristics

温度 Temperature/ ℃	品质指标 Quality index	横截面皱缩率 Cross section shrinkage rate	纵截面皱缩率 Longitudinal section shrinkage rate	色差 Chromatism	传感器2 Sensor 2	传感器6 Sensor 6	传感器7 Sensor 7	传感器9 Sensor 9
60	β-胡萝卜素含量β-Carotene content	-0.948	-0.959	-0.904	0.915	0.923	0.870	0.894
70	β-胡萝卜素含量β-Carotene content	-0.933	-0.936	-0.979	0.742	0.977	0.952	0.769
80	β-胡萝卜素含量β-Carotene content	-0.973	-0.973	-0.984	0.933	0.919	0.888	0.938

Fig.11 Scatter plot of β-carotene content predicted by the three models at 60 ℃

Table 4 Prediction results of β-carotene content in different models

模型 Model	60 ℃		70 ℃		80 ℃
模型 Model	预测集 $R p 2$	预测集根均方差 RMSEP	预测集 $R p 2$	预测集根均方差 RMSEP	预测集 $R p 2$	预测集根均方差 RMSEP
机器视觉Machine vision	0.966 7	0.981 2	0.978 5	0.962 3	0.961 2	0.956 8
电子鼻Electronic nose	0.938 1	0.904 3	0.915 4	1.395 2	0.905 0	1.016 6
多源融合Multi-source fusion	0.981 3	0.942 3	0.985 2	0.854 7	0.979 5	0.924 2

Table 4 Prediction results of β-carotene content in different models

模型 Model	60 ℃		70 ℃		80 ℃
模型 Model	预测集 $R p 2$	预测集根均方差 RMSEP	预测集 $R p 2$	预测集根均方差 RMSEP	预测集 $R p 2$	预测集根均方差 RMSEP
机器视觉Machine vision	0.966 7	0.981 2	0.978 5	0.962 3	0.961 2	0.956 8
电子鼻Electronic nose	0.938 1	0.904 3	0.915 4	1.395 2	0.905 0	1.016 6
多源融合Multi-source fusion	0.981 3	0.942 3	0.985 2	0.854 7	0.979 5	0.924 2

Fig.12 Scatter plot of β-carotene content predicted by the three models at 70 ℃

Fig.13 Scatter plot of β-carotene content predicted by the three models at 80 ℃

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Prediction of multi-source fusion of β-carotene during pumpkin drying

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