Hybrid computational intelligence algorithms and their applications to detect food quality

Goel, Lavika; Raman, Sundaresan; Dora, Subham Swastik; Bhutani, Anirudh; Aditya, Anshuman; Mehta, Abhinav

doi:10.1007/s10462-019-09705-8

Cited by 9 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors were able to optimize bioethanol production from sorghum grains, and indicated the effectiveness of the approach in reducing cost, time and effort associated with experimental techniques [186]. Further detailed description, classification and use of these AI and ML techniques is available in the literature, and can be consulted for further reading [187][188][189][190][191]. The specific and potential immediate application of AI and ML to sorghum-based fermented foods include predictive product development and optimization of fermentation processes.…”

Section: Future Projectionsmentioning

confidence: 99%

African Sorghum-Based Fermented Foods: Past, Current and Future Prospects

Adebo

2020

Nutrients

114

View full text Add to dashboard Cite

Sorghum (Sorghum bicolor) is a well-known drought and climate resistant crop with vast food use for the inhabitants of Africa and other developing countries. The importance of this crop is well reflected in its embedded benefits and use as a staple food, with fermentation playing a significant role in transforming this crop into an edible form. Although the majority of these fermented food products evolve from ethnic groups and rural communities, industrialization and the application of improved food processing techniques have led to the commercial success and viability of derived products. While some of these sorghum-based fermented food products still continue to bask in this success, much more still needs to be done to further explore evolving techniques, technologies and processes. The addition of other affordable nutrient sources in sorghum-based fermented foods is equally important, as this will effectively augment the intake of a nutritionally balanced product.

show abstract

Section: Future Projectionsmentioning

confidence: 99%

African Sorghum-Based Fermented Foods: Past, Current and Future Prospects

Adebo

2020

Nutrients

114

View full text Add to dashboard Cite

show abstract

“…The core idea is to identify the quality of food by recognizing the taste of the food. Reference [ 3 ] proposed an improved K-means algorithm for detecting spoiled food. The core idea of the study is to segment the pictures of food products in order to determine the degree of spoilage.…”

Section: Introductionmentioning

confidence: 99%

Application of Intelligent Taste Analysis Based on Random Forest Algorithm in Food Quality Inspection

Zhang

Sun

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Food safety is a major concern that has an impact on the national economy and people’s lives. The food industry has grown in quality and innovation in tandem with the rapid development of the economy and society. The emergence of new food technologies, as well as changes in dietary habits, has increased public concern about food safety. With the emergence of various counterfeit and substandard products, food quality and safety testing have become even more important. Traditional testing methods rely on sensory analysis and physical and chemical analysis. This approach is subjective and poorly adapted to the general public. It requires a high level of technical operation and is difficult to carry out on a large scale. To address this situation, this paper proposes an intelligent approach to food safety quality testing. The core idea is, first, to use sensors to collect data on the various components of the sample to be tested. Second, the random forest (RF) model used in this paper is trained. Third, the trained model is used to classify and identify the test samples. Based on the classification results, a conclusion is drawn as to whether the food product is a variant or a counterfeit. The advantage of this study is that the training model used is a weighted RF algorithm based on mutual information. The correlation between any two decision trees is calculated using mutual information, and for the more correlated decision trees, only the one with the highest evaluation accuracy is retained to form a new RF, and the evaluation accuracy is converted into voting weights, resulting in an RF model with less redundancy and higher evaluation accuracy. The experimental results show that the method used in this paper can successfully identify spoiled or counterfeit products and has good practicality.

show abstract

“…Enormous studies have been carried out to identify and classify apple diseases. For instance, Goel et al [28] proposed a method to classify healthy apples and three types of diseases (Blotch, Rot, and Scab). The authors hybridized three metaheuristic algorithms to segment apple images using the maximization function between groups for clustering and segmentation.…”

Section: Introductionmentioning

confidence: 99%

Classification of Apple Disease Based on Non-Linear Deep Features

et al. 2021

View full text Add to dashboard Cite

Diseases in apple orchards (rot, scab, and blotch) worldwide cause a substantial loss in the agricultural industry. Traditional hand picking methods are subjective to human efforts. Conventional machine learning methods for apple disease classification depend on hand-crafted features that are not robust and are complex. Advanced artificial methods such as Convolutional Neural Networks (CNN’s) have become a promising way for achieving higher accuracy although they need a high volume of samples. This work investigates different Deep CNN (DCNN) applications to apple disease classification using deep generative images to obtain higher accuracy. In order to achieve this, our work progressively modifies a baseline model by using an end-to-end trained DCNN model that has fewer parameters, better recognition accuracy than existing models (i.e., ResNet, SqeezeNet, and MiniVGGNet). We have performed a comparative study with state-of-the-art CNN as well as conventional methods proposed in the literature, and comparative results confirm the superiority of our proposed model.

show abstract

Hybrid computational intelligence algorithms and their applications to detect food quality

Cited by 9 publications

References 35 publications

African Sorghum-Based Fermented Foods: Past, Current and Future Prospects

African Sorghum-Based Fermented Foods: Past, Current and Future Prospects

Application of Intelligent Taste Analysis Based on Random Forest Algorithm in Food Quality Inspection

Classification of Apple Disease Based on Non-Linear Deep Features

Contact Info

Product

Resources

About