Visible-NIR ‘point’ spectroscopy in postharvest fruit and vegetable assessment: The science behind three decades of commercial use

Walsh, Kerry B.; Blasco, José; Zude-Sasse, Manuela; Sun, Xudong

doi:10.1016/j.postharvbio.2020.111246

Cited by 288 publications

(184 citation statements)

References 125 publications

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“…Anthocyanins responsible for red color of apples increase their consumer acceptance [33] serving also for the protection against UV and excessive sun irradiation [34,35]. In the case of fruit, the information about the fruit carried by the reflected light also translates into the perceived fruit quality (appearance, ripeness, presence of damage) [1,18,21]. Non-destructive assessment of pigments in plant in general as well as in apple fruit is complicated by overlapping light absorption by individual pigments and non-linear relationship of reflectance vs. pigment content, especially in the bands of strong absorption [2,17].…”

Section: Discussionmentioning

confidence: 99%

“…Dramatic changes in plant tissue architecture and biochemical composition during fruit development and in response to environmental stimuli adds to this complexity [33,36]. In spite of these obstacles, efficient techniques of non-invasive assessment of fruit tissue condition have been developed [18].…”

Section: Discussionmentioning

confidence: 99%

“…Attempts to apply optical reflectance spectroscopy for assessment of fruit quality and their physiological state have been undertaken for decades, but the key achievements belong mostly to the field of postharvest processes e.g. grading, sorting, storing of fruit (see [1,18] and references therein). The progress in the pre-harvest non-invasive assessment is still modest although remarkable exceptions exist [5].…”

Section: Introductionmentioning

confidence: 99%

“…Current understanding of plant spectroscopy stems mostly from the "point-based" measurements with conventional spectroradiometers and spectrophotometers [18]. Recent technical progress brought about affordable imaging hyperspectrometers (IH) providing spatially resolved spectral information on plants at different scales, from plant organs (leaves and fruits) to individual plants and ecosystems [5,19].…”

Section: Introductionmentioning

confidence: 99%

“…These uncertainties and limitations stem particularly from insufficient understanding of the relationships between biochemistry and architecture features of plant tissues and their measured optical properties [21]. Solving these problems are central to confident interpretation of reflectance images, development of computer vision and robotic systems for automated fruit harvesting and grading capable of working in orchard [5,18].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Linking Tissue Damage to Hyperspectral Reflectance for Non-Invasive Monitoring of Apple Fruit in Orchards

Solovchenko¹,

Dorokhov²,

Shurygin

et al. 2020

Preprint

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Reflected light carries ample information about biochemical composition, tissue architecture, and physiological condition of plants. Recent technical progress brought about affordable imaging hyperspectrometers (IH) providing spatially resolved spectral data on plants. The extraction of sensible information from hyperspectral reflectance images is difficult due to inherent complexity of plant tissue and canopy optics, especially when recorded by IH under ambient sunlight. We aimed at obtaining a deeper insight into plant optics as perceived by IH since there is a high demand for algorithms for fruit harvesting and grading systems equipped with computer vision and robotic systems capable of working in orchard. We report on the characteristic changes in hyperspectral reflectance accompanying the accumulation of anthocyanins in healthy fruit, pigment breakdown during sunscald and phytopathogen attacks. The measurements made outdoors with a snapshot IH were compared with traditional “point” reflectance measured with a conventional spectrophotometer under controlled illumination conditions. Most of the spectral features and patterns of plant reflectance were evident in the IH-derived reflectance images. As a step forward, a novel index for highlighting tissue damages on the background of the anthocyanin absorption, BRI-M = (1/Rorange – 1/Rred + 1/RNIR), is suggested. Difficulties of the interpretation of fruit hyperspectral reflectance images recorded in situ are discussed with possible implications for plant physiology and precision horticulture practices.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Linking Tissue Damage to Hyperspectral Reflectance for Non-Invasive Monitoring of Apple Fruit in Orchards

Solovchenko¹,

Dorokhov²,

Shurygin

et al. 2020

Preprint

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Deep chemometrics: Validation and transfer of a global deep near‐infrared fruit model to use it on a new portable instrument

Mishra

Passos

2021

Journal of Chemometrics

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Recently, a large near-infrared spectroscopy data set for mango fruit quality assessment was made available online. Based on that data, a deep learning (DL) model outperformed all major chemometrics and machine learning approaches. However, in earlier studies, the model validation was limited to the test set from the same data set which was measured with the same instrument on samples from a similar origin. From a DL perspective, once a model is trained it is expected to generalise well when applied to a new batch of data.Hence, this study aims to validate the generalisability performance of the earlier developed DL model related to DM prediction in mango on a different test set measured in a local laboratory setting, with a different instrument. At first, the performance of the old DL model was presented. Later, a new DL model was crafted to cover the seasonal variability related to fruit harvest season. Finally, a DL model transfer method was performed to use the model on a new instrument. The direct application of the old DL model led to a higher error compared to the PLS model. However, the performance of the DL model was improved drastically when it was tuned to cover the seasonal variability. The updated DL model performed the best compared to the implementation of a new PLS model or updating the existing PLS model. A final root-mean-square error prediction (RMSEP) of 0.518% was reached. This result supports that, in the availability of large data sets, DL modelling can outperform chemometrics approaches.

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Developing multifruit global near‐infrared model to predict dry matter based on just‐in‐time modeling

Mishra

2024

Journal of Chemometrics

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Modeling near‐infrared (NIR) spectral data to predict fresh fruit properties is a challenging task. The difficulty lies in creating generalized models that can work on fruits of different cultivars, seasons, and even multiple commodities of fruit. Due to intrinsic differences in spectral properties, NIR models often fail in testing, resulting in high bias and prediction errors. One current solution for achieving generalized models is to use large calibration sets measured over multiple cultivars and harvest seasons. However, current practice primarily focuses on calibration sets for single fruit commodities, disregarding the rich information available from other fruit commodities. This study aims to demonstrate the potential of locally weighted partial least‐squares an example of just‐in‐time (JIT) modeling to develop real‐time models based on calibration sets consisting of multiple fruit commodities. The study also explores JIT modeling for leveraging relevant information from other fruit commodities or adapting the model based on new samples. The application demonstrated here predicts the dry matter in fresh fruit using portable NIR spectroscopy. The results show that JIT modeling is particularly effective for multiple fruit commodities in a single calibration set. The JIT models achieved a root mean squared error of prediction (RMSEP) of 0.69% fresh weight (FW), while the traditional partial least squares (PLS) modeling RMSEP was 0.93% FW. JIT modeling can be particularly beneficial when the user has multiple fruit datasets and wants to combine them into a single dataset to utilize all the relevant information available.

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Visible-NIR ‘point’ spectroscopy in postharvest fruit and vegetable assessment: The science behind three decades of commercial use

Cited by 288 publications

References 125 publications

Linking Tissue Damage to Hyperspectral Reflectance for Non-Invasive Monitoring of Apple Fruit in Orchards

Linking Tissue Damage to Hyperspectral Reflectance for Non-Invasive Monitoring of Apple Fruit in Orchards

Deep chemometrics: Validation and transfer of a global deep near‐infrared fruit model to use it on a new portable instrument

Developing multifruit global near‐infrared model to predict dry matter based on just‐in‐time modeling

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