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FOOD CHEMISTRY · 2024 · IF 8.5

“Reference sample comparison method”: A new voltammetric electronic tongue method and its application in assessing the shelf life of fresh milk

Shiwen Cheng,Yumei Qin,Yuezhong Mao,Yanyun Cao,Ruihang Zheng,Jianzhong Han,Shiyi Tian,Zihan Qin

Shelf life is a critical comprehensive indicator of food quality. Voltammetric electronic tongue (V-Et), is well-suited for assessing food shelf life, due to its capable of capturing food overall fingerprints. This study designed a “reference sample comparison method” for V-Et to assess the shelf life of fresh milk. Quality differences between milk samples of different shelf lives and reference samples were quantified by differential degree (D d ) values. A new “one-to-one” model of milk shelf life was established based on D d values, and significantly improved predictive accuracy by 11.14 %–17.17 % and 14.86 %–44.47 % in overall quality shelf life assessment compared to “many-to-one” models based on SVM and DFA. Even in the more sophisticated evaluation of microbial safety and sensory quality shelf life, it attained relative errors of 13.57 % and 7.68 %, respectively. All these findings showed the significant potential of the “reference sample comparison method” in assessing food shelf life with V-Et.

CAS / MDL: 7647-14-5 7447-40-7
研究领域: 食品科学 · 分析化学
✓ 已验证 产品关联 DOI PubMed 高影响因子
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JOURNAL OF MAGNETIC RESONANCE · 2023 · IF 2.2

Machine learning assisted interpretation of 2D solid-state nuclear magnetic resonance spectra

Wei Tao,Wancheng Yu,Xiangyu Zou,Wei Chen

A machine learning methodology using deep neural network (DNN) for interpreting multidimensional solid-state nuclear magnetic resonance (SSNMR) of various synthetic and natural polymers is presented. The separated local field (SLF) SSNMR which correlates local well-defined heteronuclear dipolar with the tensor orientation of the chemical shift anisotropy (CSA) of spin in the solid state can provide valuable structure and molecular dynamics information of synthetic and biopolymers. Compared with the traditional linear least-square fitting, the proposed DNN-based methodology can efficiently and accurately determine the tensor orientation of CSA of both 13 C and 15 N in all four samples. The method achieves prediction precisions of the Euler angles with < ±5° and is characterized by low training costs and high efficiency (<1 s). The feasibility and robustness of the DNN-based analysis methodology are confirmed by comparison to reported-literature values. This strategy is expected to aid in the interpretation of complex multidimensional NMR spectra of complicated polymer system.

CAS / MDL: 68441-17-8
研究领域: 材料科学 · 化学 · 高分子科学 · 光谱学 · 机器学习
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