Data processing approaches and strategies for non-destructive fruits quality inspection and authentication: a review

Srivastava, Satyam; Sadistap, Shashikant

doi:10.1007/s11694-018-9893-2

Cited by 29 publications

(14 citation statements)

References 154 publications

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“…The conventional methods used to measure sugar content of melon can offer precise values, although they are mostly destructive and inefficient. 2,3 Consequently, it is essential to develop a non-destructive and efficient technique to assess the sugar content of melons for producers, distributors and consumers.…”

Section: Introductionmentioning

confidence: 99%

Non‐destructive sugar content assessment of multiple cultivars of melons by dielectric properties

Liu

Wang

et al. 2021

J Sci Food Agric

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BACKGROUND: Non-destructive determination of the internal quality of fruit with a thick rind and of a large size is always difficult and challenging. To investigate the feasibility of the dielectric spectroscopy technique with respect to determining the sugar content of melons during the postharvest stage, three cultivars of melon samples (160 melons for each cultivar) were used to acquire dielectric spectra over the frequency range 20-4500 MHz. The three cultivars of melons were divided separately into a calibration set and a prediction set in a ratio of 3:1 by a joint x-y distance algorithm. Partial least squares (PLS) and extreme learning machine (ELM) methods were applied to develop individual-cultivar and multi-cultivar models based on full frequencies (FFs) and effective dielectric frequencies (EDFs) selected by the successive projection algorithm (SPA). RESULTS:The results showed that ELM models demonstrated a better performance than PLS models for the same input dielectric variables. Most of the models built based on the EDFs selected by SPA had a slightly worse performance compared to those based on FFs. For both PLS and ELM methods, the models for multi-cultivars demonstrated a worse calibration and prediction performance compared to those for individual cultivars. When individual-cultivar and multi-cultivar samples were used to build sugar content determination models, the best model was FFs-ELM (R p = 0.887, RMSEP = 0.986), FFs-ELM (R p = 0.870, RMSEP = 1.028), FFs-PLS (R p = 0.882, RMSEP = 1.010) and FFs-ELM (R p = 0.849, RMSEP = 1.085) for 'Hongyanliang', 'Xinzaomi', 'Manao' and multi-cultivar melons, respectively. CONCLUSION: The present study indicates that it is possible to develop both individual-cultivar and multi-cultivar models for determining the sugar content of melons based on the dielectric spectroscopy technique.

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Section: Introductionmentioning

confidence: 99%

Non‐destructive sugar content assessment of multiple cultivars of melons by dielectric properties

Liu

Wang

et al. 2021

J Sci Food Agric

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“…Data fusion using AI approaches can improve the performance of analytical methods in order to evaluate the quality of food samples. In this context, food quality inspection is conducted using AI-driven tools comprising of various steps including data pre-processing, data fusion, feature extraction, and model development (Srivastava & Sadistap, 2018). AI offers an alternative solution in food quality inspection with the benefits of adaptive ability, model robustness, and self-learning ability.…”

Section: Introductionmentioning

confidence: 99%

Quality Inspection of Food and Agricultural Products using Artificial Intelligence

Ali

Hashim²,

Aziz³

et al. 2021

Adv Agric Food Res J

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A rising awareness for quality inspection of food and agricultural products has generated a growing effort to develop rapid and non-destructive techniques. Quality detection of food and agricultural products has prime importance in various stages of processing due to the laborious processes and the inability of the system to measure the whole of the food production. The detection of food quality has previously depended on various destructive techniques that require sample destruction and a large amount of postharvest losses. Artificial Intelligence (AI) has emerged with big data technologies and high-performance computation to create new opportunities in the multidisciplinary agri-food domain. This review presents the key concepts of AI comprising an expert system, artificial neural network (ANN), and fuzzy logic. A special focus is laid on the strength of AI applications in determining food quality for producing high and optimum yields. It was demonstrated that ANN provides the best result for modelling and effective in real-time monitoring techniques. The future use of AI for assessing quality inspection is promising which could lead to a real-time as well as rapid evaluation of various food and agricultural products.

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“…Fruit recognition and localization processes play important roles in the development of strawberry-harvesting robots (Gongal et al, 2015). A successful recognition and location model should avoid the misjudgment of strawberries and select suitable fruit for harvesting according to their appearance (Srivastava and Sadistap, 2018). A number of color-pattern recognition methods have emerged in the field of strawberry field-image processing, for example, the K-Nearest Neighbor algorithm, Principle Component Analysis, Linear Discriminant Analysis, and Non-Negative Matrix Factorization (Wu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A Novel Greenhouse-Based System for the Detection and Plumpness Assessment of Strawberry Using an Improved Deep Learning Technique

Zhou

et al. 2020

Front. Plant Sci.

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Data processing approaches and strategies for non-destructive fruits quality inspection and authentication: a review

Cited by 29 publications

References 154 publications

Non‐destructive sugar content assessment of multiple cultivars of melons by dielectric properties

Non‐destructive sugar content assessment of multiple cultivars of melons by dielectric properties

Quality Inspection of Food and Agricultural Products using Artificial Intelligence

A Novel Greenhouse-Based System for the Detection and Plumpness Assessment of Strawberry Using an Improved Deep Learning Technique

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