Nondestructive estimation of three apple fruit properties at various ripening levels with optimal Vis-NIR spectral wavelength regression data

Pourdarbani, Razieh; Arribas, Juan Ignacio

doi:10.1016/j.heliyon.2021.e07942

Cited by 18 publications

(10 citation statements)

References 22 publications

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“…Recently, Pourdabani et al [206] used Vis/NIR spectroscopy to nondestructively predict tissue firmness, acidity, and starch content in Fuji apples at different ripening stages. An artificial neural network-cultural algorithm (ANN-CA) was used for non-linear regression, and the results showed that the proposed method was effective in estimating fruit properties.…”

Section: Starch Pattern Index (Spi) or Starch Content Index (Sci)mentioning

confidence: 99%

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review

Grabska

Beć

Ueno

et al. 2023

Foods

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Spectroscopic methods deliver a valuable non-destructive analytical tool that provides simultaneous qualitative and quantitative characterization of various samples. Apples belong to the world’s most consumed crops and with the current challenges of climate change and human impacts on the environment, maintaining high-quality apple production has become critical. This review comprehensively analyzes the application of spectroscopy in near-infrared (NIR) and visible (Vis) regions, which not only show particular potential in evaluating the quality parameters of apples but also in optimizing their production and supply routines. This includes the assessment of the external and internal characteristics such as color, size, shape, surface defects, soluble solids content (SSC), total titratable acidity (TA), firmness, starch pattern index (SPI), total dry matter concentration (DM), and nutritional value. The review also summarizes various techniques and approaches used in Vis/NIR studies of apples, such as authenticity, origin, identification, adulteration, and quality control. Optical sensors and associated methods offer a wide suite of solutions readily addressing the main needs of the industry in practical routines as well, e.g., efficient sorting and grading of apples based on sweetness and other quality parameters, facilitating quality control throughout the production and supply chain. This review also evaluates ongoing development trends in the application of handheld and portable instruments operating in the Vis/NIR and NIR spectral regions for apple quality control. The use of these technologies can enhance apple crop quality, maintain competitiveness, and meet the demands of consumers, making them a crucial topic in the apple industry. The focal point of this review is placed on the literature published in the last five years, with the exceptions of seminal works that have played a critical role in shaping the field or representative studies that highlight the progress made in specific areas.

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Section: Starch Pattern Index (Spi) or Starch Content Index (Sci)mentioning

confidence: 99%

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review

Grabska

Beć

Ueno

et al. 2023

Foods

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“…The predicted result clearly demonstrates a strong linear correlation with a high R 2 value of 0.84, indicating that the reflectance of chlorophyll increases as the apples ripen (Figure 4c). [ 40 ] Note that a high spectral resolution leads to a low standard deviation of the reflectance spectrum, resulting in a high correlation index of R 2 value. [ 41 ] The SIG‐µSPEC clearly exhibits high correlation indices compared to the conventional handheld spectrometer (Figure S7, Supporting Information).…”

Section: Resultsmentioning

confidence: 99%

Fully Integrated Ultrathin Solid Immersion Grating Microspectrometer for Handheld Visible and Near‐Infrared Spectroscopic Applications

Park,

Jeon,

Kim

et al. 2023

Advanced Science

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Despite advances in microfabrication, compact spectrometers still face challenges in shrinking their size without sacrificing optical performance. Here, the solid immersion grating microspectrometer (SIG‐µSPEC) for high spectral resolution in a broad operational wavelength range is reported. The spectroscopic module incorporates a silicon microslit, index‐matched lens, plane mirrors, solid immersion grating (SIG), and a CMOS line sensor within a small form factor. The SIG facilitates high angular dispersion of light on a planar focal plane, resulting in an average spectral resolution of 5.8 nm, with over 76% maximum sensitivity from 400 to 800 nm. SIG‐µSPEC measures the spectral reflectance of fruits at different ripening stages, clearly revealing changes in the chlorophyll absorption band. The measured spectrum is further utilized for the precise prediction of the soluble solid content (SSC) levels, achieving a high correlation (R2 = 0.91) and a ratio of prediction‐to‐deviation of 2.36. This compact microspectrometer holds the potential for precise and non‐invasive spectral analysis across point‐of‐care fields.

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“…Moreover, the experiments also prove the positive effect of the proposed mechanism in two existing CNN models, AlexNet and VGGNet. The obtained results indicate that the use of a large training dataset (70% of the total dataset) with a small batch size (8) and a low training rate (0.0001) can improve the ability of the proposed attention mechanism to provide more accurate and reliable results. For smaller training datasets of 50% of the total, the effectiveness of the attention method may be lower.…”

Section: Discussionmentioning

confidence: 96%

“…For example, Park et al [6] applied hyperspectral reflectance data at 400-1800 nm for early detection of ginseng root rot disease, and Nguyen et al [7] used hyperspectral image (HSI) data, covering the spectral range from 400 to 780 nm, to predict the ripeness states of achacha fruit. Pourdarbani et al [8] developed a nondestructive method using HSI in the range from 400 to 1000 nm for predicting three physicochemical properties, including tissue firmness (kgf/cm), acidity (pH level), and starch content index (%) in Fuji apple (Malus M. pumila) fruit. The performance of the proposed method was evaluated using the ANN and the cultural algorithm regression model based on a reduced set of only three wavelengths.…”

Section: Introductionmentioning

confidence: 99%

Attention Mechanisms in Convolutional Neural Networks for Nitrogen Treatment Detection in Tomato Leaves Using Hyperspectral Images

et al. 2023

Self Cite

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Nitrogen is an essential macronutrient for the growth and development of tomatoes. However, excess nitrogen fertilization can affect the quality of tomato fruit, making it unattractive to consumers. Consequently, the aim of this study is to develop a method for the early detection of excessive nitrogen fertilizer use in Royal tomato by visible and near-infrared spectroscopy. Spectral reflectance values of tomato leaves were captured at wavelengths between 400 and 1100 nm, collected from several treatments after application of normal nitrogen and on the first, second, and third days after application of excess nitrogen. A new method based on convolutional neural networks (CNN) with an attention mechanism was proposed to perform the estimation of nitrogen overdose in tomato leaves. To verify the effectiveness of this method, the proposed attention mechanism-based CNN classifier was compared with an alternative CNN having the same architecture without integrating the attention mechanism, and with other CNN models, AlexNet and VGGNet. Experimental results showed that the CNN with an attention mechanism outperformed the alternative CNN, achieving a correct classification rate (CCR) of 97.33% for the treatment, compared with a CCR of 94.94% for the CNN alone. These findings will help in the development of a new tool for rapid and accurate detection of nitrogen fertilizer overuse in large areas.

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Nondestructive estimation of three apple fruit properties at various ripening levels with optimal Vis-NIR spectral wavelength regression data

Cited by 18 publications

References 22 publications

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review

Fully Integrated Ultrathin Solid Immersion Grating Microspectrometer for Handheld Visible and Near‐Infrared Spectroscopic Applications

Attention Mechanisms in Convolutional Neural Networks for Nitrogen Treatment Detection in Tomato Leaves Using Hyperspectral Images

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