Gros Michel banana soluble solids content evaluation and maturity classification using a developed portable 6 channel NIR device measurement

Sripaurya, Tanachart; Sengchuai, Kiattisak; Booranawong, Apidet; Chetpattananondh, Kanadit

doi:10.1016/j.measurement.2020.108615

Cited by 19 publications

(6 citation statements)

References 52 publications

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“…In the farming industry, visible and near-infrared (vis/NIR) spectroscopy demonstrate significant potential for sorting and grading fresh produce as per their internal attributes [ 10 , 44 , 45 ]. Over the last 2 decades, across the world, a large number of studies have been conducted to predict the sugar content in various fruits and vegetables with the help of vis/NIR techniques [ 44 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ]. 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 ).…”

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

confidence: 99%

“…A standard error of prediction for Brix values of all varieties of apples was between 0.7322 to 1.7809. Sripaurya et al [ 46 ] developed a portable 6-digital-channel NIR device to predict the quality of Gros Michel bananas and grade them by maturity stage. They also proposed a novel automatic classification of banana-ripening classification results, they where classified up to seven ripeness levels with nearly 100% accuracy.…”

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

confidence: 99%

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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%

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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“…Since the NIR lights could be greatly reflected by plant tissue, the AS7263 spectral sensor can be applied to detect the quality. Sripaurya et al [131] implemented an AS7263 spectral sensor to do the banana quality measurement by attaching the sensor to the banana peel. The AS7263 spectral sensor will emit light to banana tissue by its LED driver.…”

Section: G Fluorescence Sensormentioning

confidence: 99%

“…Then, the reflected light from banana tissue will pass through the sensor's six near the infrared channel. The reflected light intensity results are related to banana properties and maturity stages [131]. The study shows that low-cost multiple channels near-infrared (NIR) spectral sensor can be applied to estimate the quality parameters of banana fruits.…”

Section: G Fluorescence Sensormentioning

confidence: 99%

A Review on Sensing Technologies for High-Throughput Plant Phenotyping

Rayhana

Feng

et al. 2022

IEEE Open J. Instrum. Meas.

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The current epidemic, population growth, and decreasing arable lands lead to a severe food crisis, which calls for productive and efficient agricultural methods to ensure a sustainable food supply for mankind. Crop monitoring is considered to be a potential solution for the improvement of food production. Current crop monitoring combines agriculture methodologies with other advanced technologies, including sensing technology, geographical information systems (GIS), Internet of Things (IoT), information and communication technology (ICT), robotics, and drone techniques to increase production with low labor cost. The high-throughput plant phenotyping is crucial for crop monitoring on the data acquisition of large-scale crop characteristics. The high-throughput plant phenotyping studies aim to achieve fast and precise large-scale crop monitoring techniques with minimum environmental impact by applying special plant phenotyping platforms. The phenotyping platforms are integrated with various sensors and data communication systems, which can help to achieve automatic data acquisition and transmission. This paper reviews the current high-throughput plant phenotyping development in crop monitoring, including sensors, communication protocols, data management, and plant phenotyping platforms. State-of-art challenges are reviewed and discussed. Also, the paper provides discussions on the current situation, upcoming challenges, and possible future trends for researchers in this field.

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“…Over the past decades, NIR spectroscopy has gained considerable attention for non-destructive maturity index assessment due to its ease, fast detection speed and precision [7,8]. Researchers have used NIR spectroscopy with machine learning regression algorithms to develop maturity index prediction models such as DM, Brix, color, chlorophyll, starch and TA (only in high acid fruit like lemon and mandarin) of various fruits including apple [9], pear [10], nectarine [11], mango [12], banana [13], melon [14], mandarin [15], strawberry [16], apricot [17], kiwifruit [18], persimmon [19], grape [20], loquat [21] and pineapple [22].…”

Section: Introductionmentioning

confidence: 99%

Towards Sensory Assessment Classification using Short-Wave NIR Spectroscopy for Orange Cultivars

Zeb

Qureshi

Ghafoor

et al. 2022

Preprint

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The global orange industry constantly faces new technical challenges to meet consumer demands for quality fruits. Instead of traditional subjective fruit quality assessment methods, the interest in the horticulture industry has increased in objective, quantitative, and non-destructive assessment methods. Oranges have a thick peel which makes their non-destructive quality assessment challenging. This paper evaluates the potential of short-wave NIR spectroscopy and direct sweetness classification for Pakistani cultivars of orange i.e., Blood red, Mosambi, and Succari. The correlation between quality indices i.e., Brix, titratable acidity (TA), Brix: TA and BrimA (Brix minus acids), sensory assessment of the fruit, and short-wave NIR spectra is analyzed. Mix cultivar oranges are then classified as sweet, mixed, and acidic based on short-wave NIR spectra. Short-wave NIR spectral data was obtained using the industry standard F-750 fruit quality meter (310-1100 nm). Reference Brix and TA measurements were taken using standard destructive testing methods. Reference taste labels i.e., sweet, mix and acidic, were acquired by sensory evaluation of samples. For indirect fruit classification, partial least squares regression models were developed for Brix, TA, Brix:TA and BrimA estimation with a correlation coefficient of 0.57, 0.73, 0.66 and 0.55 respectively, on independent test data. For direct fruit classification, the ensemble classifier achieved 81.03% accuracy for 3 class (sweet, mix and acidic) classification on independent test data. We observed a good correlation between NIR spectra and sensory assessment instead of quality indices. Hence, direct classification is more suitable for orange sweetness classification using NIR spectroscopy than the estimation of quality indices.

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Gros Michel banana soluble solids content evaluation and maturity classification using a developed portable 6 channel NIR device measurement

Cited by 19 publications

References 52 publications

Sensors and Instruments for Brix Measurement: A Review

Sensors and Instruments for Brix Measurement: A Review

A Review on Sensing Technologies for High-Throughput Plant Phenotyping

Towards Sensory Assessment Classification using Short-Wave NIR Spectroscopy for Orange Cultivars

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