Perspective - Journal of Biotechnology and Phytochemistry (2023) Volume 7, Issue 4

Non-destructive detection of fruit quality indicated by electronic and color-metric properties

Neha Malhotra^*

Department of Plant Biotechnology

*Corresponding Author:

Neha Malhotra
Department of Plant Biotechnology
University of Delhi
India
E-mail:neham21@gmail.com

Received:29-Jul-2023, Manuscript No. AAJBP-23-109258; Editor assigned:03-Aug-2023, PreQC No. AAJBP-23-109258(PQ); Reviewed:17-Aug-2023, QC No. AAJBP-23-109258; Revised:22-Aug-2023, Manuscript No. AAJBP-23-109258(R); Published:30-Aug-2023, DOI:10.35841/ aajbp-7.4.159

Citation: Malhotra N. Non-destructive detection of fruit quality indicated by electronic and color-metric properties. J Biotech and Phytochem. 2023;7(4):159

Introduction

Fruit quality is a vital aspect of the agricultural industry, influencing consumer satisfaction, shelf-life, and economic value. Traditional methods of assessing fruit quality often involve destructive testing, which not only results in the loss of the tested fruit but also fails to provide real-time feedback to farmers and distributors. In recent years, non-destructive techniques that rely on electronic and color-metric properties have emerged as promising alternatives for evaluating fruit quality without compromising the product's integrity. These methods offer rapid, accurate, and efficient ways to assess various attributes such as ripeness, freshness, and nutritional content [1].

Electronic property-based assessment

Electronic properties of fruits, such as electrical conductivity, impedance, and capacitance, have been extensively studied for their correlation with internal quality attributes. These properties are influenced by factors like moisture content, sugar content, and ion concentration, which in turn affect the taste, texture, and nutritional value of the fruit. By measuring these electronic properties, non-destructive techniques can provide insights into the fruit's overall quality. One such technique is Electrical Impedance Spectroscopy (EIS), which involves passing a small electric current through the fruit and measuring its impedance response across a range of frequencies. Changes in impedance are indicative of changes in tissue structure and moisture content. For instance, as fruits ripen and water content decreases, impedance tends to increase. By analyzing impedance data, farmers and producers can determine the optimal harvesting time and storage conditions for fruits [2].

Color-metric property-based assessment

Color is a key external indicator of fruit quality and is closely linked to factors such as ripeness, freshness, and health. Traditional methods of color assessment involve subjective human judgment, which can be inconsistent and prone to errors. Color-metric techniques, on the other hand, utilize objective measurements and advanced algorithms to precisely quantify color attributes. Color analysis involves capturing images of fruits using cameras or spectrometers and then analyzing the data to extract color-related metrics such as hue, saturation, and brightness. These metrics can provide insights into the fruit's internal attributes, such as chlorophyll content and ripeness. For example, a decrease in greenness and an increase in redness might indicate ripening in certain types of fruits. By monitoring color changes, producers can optimize post-harvest handling and distribution processes [3].

The adoption of non-destructive techniques for assessing fruit quality offers several advantages. Firstly, it reduces wastage as fruits remain intact and can still be sold after testing. Secondly, it enables real-time monitoring, allowing for better decision-making during harvesting, processing, and distribution. Additionally, these methods can be integrated into automated systems, enhancing efficiency and reducing labor costs. As technology continues to advance, non-destructive methods are likely to become even more sophisticated and accurate. Machine learning and artificial intelligence algorithms can be employed to develop predictive models that correlate electronic and color-metric data with specific quality attributes. This would enable quick and reliable assessments that go beyond simple visual inspection [4].

While non-destructive techniques offer numerous benefits, they also come with certain challenges and considerations. Calibration and validation of the electronic and color-metric measurements are crucial to ensure accuracy and consistency across different fruit varieties and growing conditions. Factors like temperature, humidity, and lighting conditions can influence the readings and need to be standardized. Additionally, the cost of implementing these techniques can be a barrier, especially for small-scale farmers. The initial investment in specialized equipment and training might deter some producers from adopting these methods. Therefore, efforts to make the technology more accessible and affordable could accelerate its widespread adoption. Furthermore, the relationship between electronic and color-metric properties and specific quality attributes can be complex and may vary depending on the fruit type. Research and data collection are necessary to establish robust correlations and predictive models. Collaborations between agricultural scientists, engineers, and software developers are essential to refine and optimize these techniques [5].

Conclusion

Non-destructive detection of fruit quality through electronic and color-metric properties is a groundbreaking advancement in the agriculture industry. These techniques provide valuable insights into the internal and external attributes of fruits, allowing for informed decision-making, quality control, and waste reduction. While challenges such as calibration, cost, and data correlation need to be addressed, the potential benefits for producers, consumers, and the environment are substantial.

References

1. Huang H, Liu L, Ngadi MO. Recent developments in hyperspectral imaging for assessment of food quality and safety.. Sensors. 2014;14(4):7248-76.

Indexed at, Google Scholar, Cross Ref

2. Fu XP, Ying YB, Zhou Y, et al. Application of NIR spectroscopy for firmness evaluation of peaches. Zhejiang Univ Sci B. 2008;9:552-7.

Indexed at, Google Scholar, Cross Ref

3. El-Mesery HS, Mao H, Abomohra AE. Applications of non-destructive technologies for agricultural and food products quality inspection. Sensors. 2019;19(4):846.

Indexed at, Google Scholar, Cross Ref

4. Zhao Y, Gong L, Huang Y, et al. Robust tomato recognition for robotic harvesting using feature images fusion. Sensors. 2016;16(2):173.

Indexed at, Google Scholar, Cross Ref

5. Marques EJ, de Freitas ST, Pimentel MF, et al. .Rapid and non-destructive determination of quality parameters in the ‘Tommy Atkins’ mango using a novel handheld near infrared spectrometer. Food Chem. 2016;197:1207-14.

Indexed at, Google Scholar, Cross Ref