A Portable Spectrometric System for Quantitative Prediction of the Soluble Solids Content of Apples with a Pre-calibrated Multispectral Sensor Chipset

Tran, Nhut-Thanh; Fukuzawa, Masahiro

doi:10.3390/s20205883

Cited by 23 publications

(6 citation statements)

References 32 publications

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“…The method uses visible and NIR range of the electromagnetic spectrum, covering approximately 300–2500 nm to measure the sugar content of different fruits such as apples, bananas, pomegranates, kiwifruit, mandarins, mangoes, oranges, pineapple, sugarcane, and tomatoes ( Table 4 ). The NIR spectral acquisition from the sample is performed with the help of three standard modes—transmission, reflectance, and interactance [ 59 ]. In the transmission mode, while infrared light is passing through a sample, some part of the light can be absorbed by the sample and the remaining fraction is measured on the other side of the sample.…”

Section: Nondestructive Methods For Brix Measurement In Fruits and Ve...mentioning

confidence: 99%

Sensors and Instruments for Brix Measurement: A Review

Jaywant

Singh

Arif

2022

Sensors

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Quality assessment of fruits, vegetables, or beverages involves classifying the products according to the quality traits such as, appearance, texture, flavor, sugar content. The measurement of sugar content, or Brix, as it is commonly known, is an essential part of the quality analysis of the agricultural products and alcoholic beverages. The Brix monitoring of fruit and vegetables by destructive methods includes sensory assessment involving sensory panels, instruments such as refractometer, hydrometer, and liquid chromatography. However, these techniques are manual, time-consuming, and most importantly, the fruits or vegetables are damaged during testing. On the other hand, the traditional sample-based methods involve manual sample collection of the liquid from the tank in fruit/vegetable juice making and in wineries or breweries. Labour ineffectiveness can be a significant drawback of such methods. This review presents recent developments in different destructive and nondestructive Brix measurement techniques focused on fruits, vegetables, and beverages. It is concluded that while there exist a variety of methods and instruments for Brix measurement, traits such as promptness and low cost of analysis, minimal sample preparation, and environmental friendliness are still among the prime requirements of the industry.

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Section: Nondestructive Methods For Brix Measurement In Fruits and Ve...mentioning

confidence: 99%

Sensors and Instruments for Brix Measurement: A Review

Jaywant

Singh

Arif

2022

Sensors

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“…Thus, a setup of AS7265x multispectral sensors (AMS, Premstaetten, Austria; b-type in Figure 2 and Table 1) has been compared with an Ocean Optics USB2000+ (Ocean Insight, Orlando, USA) dispersive spectrometer (classical a-type in Figure 2 and Table 1) for the assessment of SSC in apples [126]. The two pieces of equipment delivered similar performance.…”

Section: Emerging Technologies For Portable Spectroscopymentioning

confidence: 99%

Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture

et al. 2022

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As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.

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“…A nondestructive spectroscopic system [ 16 ] was used to predict the SSC of apple samples. An optical setup was used to obtain the multispectral data from the samples.…”

Section: Introductionmentioning

confidence: 99%

Classification of Organic and Conventional Vegetables Using Machine Learning: A Case Study of Brinjal, Chili and Tomato

Natarajan

Ponnusamy

2023

Foods

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Growing organic food is becoming a challenging task with increasing demand. Food fraud activity has increased considerably with the increase in population growth. Consumers cannot visually distinguish between conventional and organically grown food products. Spectroscopic methodologies are presented to identify chemicals in food, thereby identifying organic and conventional food. Such spectroscopic techniques are laboratory-based, take more time to produce an outcome, and are costlier. Thus, this research designed a portable, low-cost multispectral sensor system to discriminate between organic and conventional vegetables. The designed multispectral sensor system uses a wavelength range (410 nm–940 nm) that includes three bands, namely visible (VIS), ultraviolet (UV) and near-infrared (NIR) spectra, to enhance the accuracy of detection. Tomato, brinjal and green chili samples are employed for the experiment. The organic and conventional discrimination problem is formulated as a classification problem and solved through random forest (RF) and neural network (NN) models, which achieve 92% and 89% accuracy, respectively. A two-stage enhancement mechanism is proposed to improve accuracy. In the first stage, the fuzzy logic mechanism generates additional feature sets. Ant colony optimization (ACO) algorithm-based parameter tuning and feature selection are employed in the second stage to enhance accuracy further. This two-stage improvement mechanism results in 100% accuracy in discriminating between organic and conventional vegetable samples. The detected adulterant is displayed on a web page through an IoT-developed application module to be accessed from anywhere.

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A Portable Spectrometric System for Quantitative Prediction of the Soluble Solids Content of Apples with a Pre-calibrated Multispectral Sensor Chipset

Cited by 23 publications

References 32 publications

Sensors and Instruments for Brix Measurement: A Review

Sensors and Instruments for Brix Measurement: A Review

Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture

Classification of Organic and Conventional Vegetables Using Machine Learning: A Case Study of Brinjal, Chili and Tomato

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