Validation of short wave near infrared calibration models for the quality and ripening of ‘Newhall’ orange on tree across years and orchards

Cavaco, Ana M.; Pires, Rosa; Antunes, Maria Dulce; Panagopoulos, Τhomas; Brázio, António; Afonso, Andreia M.; Silva, Leonardo; Lucas, Márcia Rosendo; Cadeiras, Bernardo; Cruz, Sandra; Guerra, Rui

doi:10.1016/j.postharvbio.2018.03.013

Cited by 40 publications

(31 citation statements)

References 39 publications

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“…The interferences of solid particle size, surface scattering, and the change of optical path of diffuse reflection spectra can be eliminated by SNV method. The results are consistent with the related reference(Cavaco et al, 2018).…”

supporting

confidence: 93%

“…Vis/NIRS has attracted more and more attention due to its fast and nondestructive characteristics in the analysis of soluble solid content in citrus (Cavaco et al, 2018;Cayuela & Weiland, 2010;Cen, He, & Huang, 2007). The prediction of soluble solid content in citrus by Vis/NIRS and sensory test was investigated and the result shows that the nondestructive method can meet the sensory requirements of consumers (Yuan et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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A simple and nondestructive approach for the analysis of soluble solid content in citrus by using portable visible to near‐infrared spectroscopy

et al. 2020

Food Science & Nutrition

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A simple and nondestructive method for the analysis of soluble solid content in citrus was established using portable visible to near‐infrared spectroscopy (Vis/NIRS) in reflectance mode in combination with appropriate chemometric methods. The spectra were obtained directly by the portable Vis/NIRS without destroying samples. Outlier detection was performed by using leave‐one‐out cross‐validation (LOOCV) with the 3σ criterion, and the calibration models were established by partial least squares (PLS) algorithm. Besides, different data pretreatment methods were used to eliminate noise and background interference before calibration, to determine the one that will lead to better model accuracy. However, the correlation coefficients are all <0.62 and the results of all pretreatments are still unsatisfactory. Variable selection methods were discussed for improving the accuracy, and variable adaptive boosting partial least squares (VABPLS) method was used to get higher robustness models. The results show that standard normal variate (SNV) transformation is the best pretreatment method, while VABPLS can significantly simplify the calculation and improve the result even without pretreatment. The correlation coefficient of the best prediction models is 0.82, while the value is 0.48 for the raw data. The high performance shows the feasibility of portable Vis/NIRS technology combination with appropriate chemometric methods for the determination of citrus soluble solid content.

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supporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

A simple and nondestructive approach for the analysis of soluble solid content in citrus by using portable visible to near‐infrared spectroscopy

et al. 2020

Food Science & Nutrition

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“…In the case of the acidity, the most related works are due to Huang et al [36], Cavaco et al [38] and Cayuela [23]. Table 16 shows the comparison of the proposed method and these works.…”

Section: Non-destructive Estimation Of the Aciditymentioning

confidence: 99%

“…Proposed method Apple 841 to 855 nm 0.825 Huang et al [36] Tomato 400 to 1100 nm 0.64 Cavaco et al [38] Orange 733 to 1000 nm 0.52 Cayuela [23] Orange 578 to 1850 nm 0.45 Coefficient of determination, R 2.…”

Section: Researchersmentioning

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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“…Nowadays, agricultural automation is inevitable to reduce costs, minimize labor difficulty, decrease environmental impact, increase timely farming and crop quality, and brink transparency in the supply chain [1,2]. One of the challenges of robots is to identify and harvest the ripe fruits.…”

Section: Introductionmentioning

confidence: 99%

Designing a Fruit Identification Algorithm in Orchard Conditions to Develop Robots Using Video Processing and Majority Voting Based on Hybrid Artificial Neural Network

et al. 2020

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The first step in identifying fruits on trees is to develop garden robots for different purposes such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit orchards and the unevenness of the various objects throughout it, usage of the controlled conditions is very difficult. As a result, these operations should be performed in natural conditions, both in light and in the background. Due to the dependency of other garden robot operations on the fruit identification stage, this step must be performed precisely. Therefore, the purpose of this paper was to design an identification algorithm in orchard conditions using a combination of video processing and majority voting based on different hybrid artificial neural networks. The different steps of designing this algorithm were: (1) Recording video of different plum orchards at different light intensities; (2) converting the videos produced into its frames; (3) extracting different color properties from pixels; (4) selecting effective properties from color extraction properties using hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue (LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation criteria of the classifiers, it was found that the majority voting method had a higher performance.

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Validation of short wave near infrared calibration models for the quality and ripening of ‘Newhall’ orange on tree across years and orchards

Cited by 40 publications

References 39 publications

A simple and nondestructive approach for the analysis of soluble solid content in citrus by using portable visible to near‐infrared spectroscopy

A simple and nondestructive approach for the analysis of soluble solid content in citrus by using portable visible to near‐infrared spectroscopy

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

Designing a Fruit Identification Algorithm in Orchard Conditions to Develop Robots Using Video Processing and Majority Voting Based on Hybrid Artificial Neural Network

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