Incorporating Bioimpedance Technique with Ensemble Learning Algorithm for Mutton Tenderness Detection

Liang, Buwen; Wei, Changhui; Li, Xinxing; Zhang, Ziyi; Huang, Xiaoyan

doi:10.1007/s11947-023-03065-6

Food Bioprocess Technol

2023

DOI: 10.1007/s11947-023-03065-6

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Incorporating Bioimpedance Technique with Ensemble Learning Algorithm for Mutton Tenderness Detection

Buwen Liang

Changhui Wei

Xinxing Li

et al.

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2024

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Cited by 2 publications

References 40 publications

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Digital Food Sensing and Ingredient Analysis Techniques to Facilitate Human-Food Interface Designs

Amarasinghe,

Ranasinghe

2024

ACM Comput. Surv.

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Interactive technologies that shape the traditional human-food experiences are being explored under the emerging field of Human-Food Interaction (HFI). A key challenge in developing HFI technologies is the digital sensing of food, beverages, and their ingredients, commonly known as digital food sensing. Digital food sensing involves recognizing different food and beverages and their internal attributes, such as volume and ingredients (e.g., salt and sugar content). Contemporary research on interactive food applications, such as dietary assessment, primarily employs Computer Vision (CV) techniques to identify food; however, they are ineffective when 1) identifying food’s internal attributes, 2) discriminating visually similar food and beverages, and 3) seamlessly integrating with people’s natural interactions while consuming food. Thus, this paper reviews potential food and beverage sensing technologies that can facilitate novel Human-Food Interfaces, primarily focusing on non-disruptive sensing techniques to analyze food and beverages during consumption. First, we review ten different digital food sensing techniques and their applications in four categories. Then, we discuss three main aspects to consider when adopting these food-sensing techniques for human-food interface designs. Finally, we suggest the future research requirements in digital food sensing methodologies, followed by potential applications of digital food sensing in future developments of Human-Food Interfaces.

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Digital Food Sensing and Ingredient Analysis Techniques to Facilitate Human-Food Interface Designs

Amarasinghe,

Ranasinghe

2024

ACM Comput. Surv.

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Explainable Feature Engineering for Multi-Modal Tissue State Monitoring Based on Impedance Spectroscopy

Guermazi,

Kallel,

Kanoun

2024

Sensors

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One of the most promising approaches to food quality assessments is the use of impedance spectroscopy combined with machine learning. Thereby, feature selection is decisive for a high classification accuracy. Physically based features have particularly significant advantages because they are able to consider prior knowledge and to concentrate the data into pertinent understandable information, building a solid basis for classification. In this study, we aim to identify physically based measurable features for muscle type and freshness classifications of bovine meat based on impedance spectroscopy measurements. We carry out a combined study where features are ranked based on their F1-score, cumulative feature selection, and t-distributed Stochastic Neighbor Embedding (t-SNE). In terms of features, we analyze the characteristic points (CPs) of the impedance spectrum and the model parameters (MPs) obtained by fitting a physical model to the measurements. The results show that either MPs or CPs alone are sufficient for detecting muscle type. Combining capacitance (C) and extracellular resistance (Rex) or the modulus of the characteristic point Z1 and the phase at the characteristic frequency of the beta dispersion (Phi2) leads to accurate separation. In contrast, the detection of freshness is more challenging. It requires more distinct features. We achieved a 90% freshness separation using the MPs describing intracellular resistance (Rin) and capacitance (C). A 95.5% freshness separation was achieved by considering the phase at the end of the beta dispersion (Phi3) and Rin. Including additional features related to muscle type improves the separability of samples; ultimately, a 99.6% separation can be achieved by selecting the appropriate features.

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Incorporating Bioimpedance Technique with Ensemble Learning Algorithm for Mutton Tenderness Detection

Cited by 2 publications

References 40 publications

Digital Food Sensing and Ingredient Analysis Techniques to Facilitate Human-Food Interface Designs

Digital Food Sensing and Ingredient Analysis Techniques to Facilitate Human-Food Interface Designs

Explainable Feature Engineering for Multi-Modal Tissue State Monitoring Based on Impedance Spectroscopy

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