A TSS classification study of ‘Rocha’ pear ( Pyrus communis L.) based on non-invasive visible/near infra-red reflectance spectra

Bexiga, Florentino; Rodrigues, Daniela; Guerra, Rui; Brázio, António; Balegas, Tiago; Cavaco, Ana M.; Antunes, Maria Dulce; Oliveira, José Valente de

doi:10.1016/j.postharvbio.2017.05.014

Cited by 24 publications

(11 citation statements)

References 10 publications

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“…The importance of fruit sorting and grading is increasing in the agro-industry and global economy. The classification of fruits based on their internal and external qualitative features enables intelligent management at the different stages of fruit distribution and processing [1]. The appearance, color, size and absence of defects on fruits are some of the most important qualitative characteristics based on external fruit properties [2].…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Constituent Properties of Red Delicious Apples Using a Hybrid of Artificial Neural Networks and Artificial Bee Colony Algorithm

et al. 2020

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Non-destructive estimation of the constituent properties of fruits and vegetables has led to a dramatic change in the agriculture and food industry, allowing accurate and efficient sorting of the products based on their internal properties. Therefore, the present study utilized visible (VIS) and near-infrared (NIR) spectroscopy data in the range from 200 to 1100 nm for the estimation of several properties of Red Delicious apples, namely Brix minus acid (BrimA), firmness, acidity and starch content, using a hybrid of Artificial Neural Networks and Artificial Bee Colony (ANN–ABC) algorithm. Furthermore, the hybrid Artificial Neural Network–Particle Swarm Optimization (ANN–PSO) algorithm was utilized to select the most effective properties to estimate these characteristics. The results indicated that there are different peaks within this spectral range, and the spectral range for each peak gives different results. To ensure the stability of the proposed method, 1000 replications were performed for each estimate. The highest coefficients of determination, R2, for estimating the studied properties among the 1000 replicates were 0.898 for BrimA, 0.8 for firmness, 0.825 for acidity and 0.973 for starch content. The selection of the most effective wavelengths for estimating the properties produced five effective wavelengths for BrimA, nine for firmness, seven for acidity and five for starch content. In this case, the best R2 of the hybrid ANN–ABC among the 1000 iterations were 0.828, 0.738, 0.9 and 0.923, respectively.

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Section: Introductionmentioning

confidence: 99%

Estimation of the Constituent Properties of Red Delicious Apples Using a Hybrid of Artificial Neural Networks and Artificial Bee Colony Algorithm

et al. 2020

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“…All the growth regulation treatments except Ethrel (50 ppm), Ethrel (25 ppm), jeevamrit, wastedecomposer, and NAA (25 ppm) had significantly ( p < .05) higher tuber dry-matter than the control. The composition of total soluble solids (TSS) is predominantly made up of the overall sugars, with a small proportion of soluble protein, amino acids, and other organic matter (Bexiga et al, 2017). The specific gravity of potato tubers is a crucial qualitative factor for seed and processing potato.…”

Section: Physiological Attributesmentioning

confidence: 99%

Exogenous growth regulators amplify the morpho‐physiology, root architecture and dry‐matter accumulation in seed potato

Singh,

Aulakh,

Chawla

et al. 2023

Annals of Applied Biology

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Enhancing the productivity of seed potato production systems by improving the size of the tubers is a primary concern. Punjab, a state located in the northwest region of India, meets 60 percent of the country's seed requirements. In order to optimize the morpho‐physiological aspects, root structure, and overall yield of seed‐sized tubers, research trials were carried out at Punjab Agricultural University, Ludhiana over two consecutive years. The trial consisted of 11 different treatments and was conducted using a randomized complete block design with three replications. The application of Gibberellic acid at a concentration of 200 ppm, at 45 and 60 days after sowing, resulted in significantly higher yields of seed‐sized tubers measuring less than 4.5 cm. This treatment also exhibited superior root architecture parameters and accumulation of dry matter, outperforming all other growth regulation treatments. Compared to the untreated control group, the Gibberellic acid application led to a remarkable increase of 45.9 and 39.8 percent in seed‐sized tuber yields. Furthermore, it significantly boosted the protein, starch, antioxidant (total phenols), sugar (total and reducing), and mineral content (nitrogen, phosphorus, and potassium) in the tubers, surpassing the effects of all other growth regulation treatments. These results suggested that the exogenous use of gibberellic acid (200 ppm) at 45 and 60 days after sowing substantially improved the physiology, root architecture, and yield of seed‐sized tubers.This article is protected by copyright. All rights reserved.

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“…Vis-NIR spectroscopy has been used for different purposes on different fruits such as orange [16], kiwi [17], apricot [18], pear [19], and apple [20]. Vis-NIR spectroscopy was used by Uwadaira et al [21] 2 of 16 for non-destructive evaluation of peach fruit.…”

Section: Introductionmentioning

confidence: 99%

Determination of the Most Effective Wavelengths for Prediction of Fuji Apple Starch and Total Soluble Solids Properties

Pourdarbani

Sabzi

Jarolmasjed³

et al. 2020

Applied Sciences

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Proper physical properties and standard chemical properties are among the criteria that consumers use to select fruits. Recently, researchers attempted to develop non-destructive methods for measuring properties, among which the near-infrared (NIR) spectroscopy is of great use. Fuji apples were collected in three different growth stages, and then starch and soluble solids were extracted. Spectral data in the range of 800 to 900 nm were used to predict the amount of starch content and 920 to 980 nm to estimate total soluble solids (TSS). Reflectance spectra were pre-processed and the most effective wavelengths of each property were selected using hybrid artificial neural network-simulated annealing (ANN-SA). Non-destructive estimation of physicochemical properties was conducted using spectral data of the most effective wavelengths using a hybrid artificial neural network-biogeography-based optimization algorithm (ANN-BBO). The results indicated that the regression coefficient of the best state of training for predicting starch was 0.97 and of TSS was 0.96, while R2 was 0.92 for both. The most effective wavelengths were 852.58, 855.54, 849.03, 855.83, 853.47, 844.90 nm for starch and 967.86, 966.67, 964.90, 958.40, 957.22, 963.97 nm for TSS.

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A TSS classification study of ‘Rocha’ pear ( Pyrus communis L.) based on non-invasive visible/near infra-red reflectance spectra

Cited by 24 publications

References 10 publications

Estimation of the Constituent Properties of Red Delicious Apples Using a Hybrid of Artificial Neural Networks and Artificial Bee Colony Algorithm

Estimation of the Constituent Properties of Red Delicious Apples Using a Hybrid of Artificial Neural Networks and Artificial Bee Colony Algorithm

Exogenous growth regulators amplify the morpho‐physiology, root architecture and dry‐matter accumulation in seed potato

Determination of the Most Effective Wavelengths for Prediction of Fuji Apple Starch and Total Soluble Solids Properties

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