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Rapid non-destructive detection of mildly moldy Zea mays by near-infrared spectroscopy technology
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Jie LI1, 2, 3, Chun GAO3, Li XU3, Han-Lin ZHU3, Min PANG1, 2, 3, Li-Li CAO1, 2, 3, *
Journal of Food Safety & Quality | 2025, 16(4) : 18 - 25
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Journal of Food Safety & Quality | 2025, 16(4): 18-25
Special Topic: Grain and Oil Processing and Quality Safety
Rapid non-destructive detection of mildly moldy Zea mays by near-infrared spectroscopy technology
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Jie LI1, 2, 3, Chun GAO3, Li XU3, Han-Lin ZHU3, Min PANG1, 2, 3, Li-Li CAO1, 2, 3, *
Affiliations
  • 1. School of Food and Bioengineering, Hefei University of Technology, Hefei 230601, China
  • 2. Anhui Province Key Laboratory of Agricultural Products Modern Processing, Hefei 230601, China
  • 3. Anhui Province Key Laboratory of Intelligent Green Quality Sorting Technology and Equipment for Agricultural Products, Hefei 230071, China
Published: 2025-02-25 doi: 10.19812/j.cnki.jfsq11-5956/ts.20241106008
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Objective To rapidly and non-destructively detect aflatoxin in mildly moldy Zea mays using near-infrared spectroscopy (NIRS) technology. Methods Mildly moldy Zea mays samples were selected as experimental materials, with the content of aflatoxin B1 (AFB1) as the detection indicator. A total of 153 sample images were collected using the NIRS imaging acquisition system. Three kinds of preprocessing methods, including multiplicative scatter correction, standard normal variate transformation, and moving average smoothing (MAS), were applied to preprocess the raw near-infrared spectral data (RNSD). Backpropagation neural network (BPNN), extreme learning machine, and support vector machine were employed to model and analyze the preprocessed spectral data along with AFB1 content data, evaluating the impact of preprocessing methods on model performance. Furthermore, the stepwise projection algorithm (SPA) was performed to select characteristic spectra from the preprocessed data for comprehensive comparison after incorporating them into the models. Results The optimal spectral preprocessing method was MAS. Ten characteristic spectra were selected through SPA, and the BPNN model exhibited the best prediction results, achieving a coefficient of determination of 0.932 and a relative prediction deviation of 3.922. This model demonstrated good performance and reliability. Conclusion It is feasible to determine AFB1 content in mildly moldy Zea mays using NIRS technology. The findings of this study provide an important reference for the application of NIRS in identifying other agricultural products.

Zea mays  /  near-infrared spectroscopy  /  aflatoxin  /  backpropagation neural network
Jie LI, Chun GAO, Li XU, Han-Lin ZHU, Min PANG, Li-Li CAO. Rapid non-destructive detection of mildly moldy Zea mays by near-infrared spectroscopy technology[J]. Journal of Food Safety & Quality, 2025 , 16 (4) : 18 -25 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20241106008
Year 2025 volume 16 Issue 4
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Article Info
doi: 10.19812/j.cnki.jfsq11-5956/ts.20241106008
  • Receive Date:2024-11-06
  • Online Date:2025-07-21
  • Published:2025-02-25
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  • Received:2024-11-06
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Affiliations
    1. School of Food and Bioengineering, Hefei University of Technology, Hefei 230601, China
    2. Anhui Province Key Laboratory of Agricultural Products Modern Processing, Hefei 230601, China
    3. Anhui Province Key Laboratory of Intelligent Green Quality Sorting Technology and Equipment for Agricultural Products, Hefei 230071, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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