Self-adaptive models for predicting soluble solid content of blueberries with biological variability by using near-infrared spectroscopy and chemometrics

Zheng, Wei; Bai, Yuhao; Luo, Hui; Li, Yuhua; Yang, Xi; Zhang, Baohua

doi:10.1016/j.postharvbio.2020.111286

Cited by 31 publications

(9 citation statements)

References 36 publications

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“…The model failure for fresh fruit is commonly related to a high biological variability which can be related to several factors such as: cultivars, sites of cultivation, cultural practices, season of harvest, ripening stages of fruit, and storage conditions [37,76,77]. Consequently, a natural solution to deal with the calibration failure is to measure a wide range of samples from different cultivars and harvesting seasons (2-3 seasons) and developing/ripening stages to calibrate global models to be used worldwide.…”

Section: Resultsmentioning

confidence: 99%

Ripeness Prediction in Table Grape Cultivars by Using a Portable NIR Device

Ferrara

Marcotuli

Didonna³

et al. 2022

Horticulturae

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In the past years, near infrared (NIR) spectroscopy has been applied to the agricultural industry as a non-destructive tool to predict quality parameters, e.g., ripeness of fruit, dry matter content, and acidity. In two years, 2019 and 2020, berries of four table grape cultivars (Cotton Candy™, Summer Royal, Allison™, and Autumncrisp®) were collected during the season to obtain spectral measurements and quality data for developing predictive models based on NIR spectroscopy to be practically used in the vineyard. A SCiO™ sensor was used in 2019 for predicting the ripening parameters of Cotton Candy™; in particular, total soluble solids (TSS) (R2 = 0.95; RMSE = 0.60, RPD = 13.13), titratable acidity (R2 = 0.97; RMSE = 0.40, RPD = 7.31), and pH (R2 = 0.96; RMSE = 0.07, RPD = 26.06). With these promising results, in the year 2020, the above-mentioned table grape cultivars were all tested for TSS prediction with successful outcomes: Cotton Candy™ (R2 = 0.97; RMSE = 0.68, RPD = 7.48), Summer Royal (R2 = 0.96; RMSE = 0.83, RPD = 7.13), Allison™ (R2 = 0.97; RMSE = 0.72, RPD = 8.70) and Autumncrisp® (R2 = 0.96; RMSE = 0.60, RPD = 9.73). In conclusion, a rapid and economic sensor such as the SCiO™ device can enable a practical application in the vineyard to assess ripening (quality) parameters of table grapes. Thus, this device or similar ones can be also used for a fast sorting and screening of quality throughout the supply chain, from vineyard to cold storage.

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Section: Resultsmentioning

confidence: 99%

Ripeness Prediction in Table Grape Cultivars by Using a Portable NIR Device

Ferrara

Marcotuli

Didonna³

et al. 2022

Horticulturae

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“…More recently, NIR detection technology has been widely used in nondestructive testing of blueberries’ physical and chemical properties and for the detection of SSC (Hu et al., 2016; Zheng et al., 2020), anthocyanins (Bai et al., 2014), vitamins and total acid (TA) (L. Li et al., 2023), internal bruising (Fan et al., 2018), and hardness (L. Xue et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

Modeling of soluble solid content of PE‐packaged blueberries based on near‐infrared spectroscopy with back propagation neural network and partial least squares (BP–PLS) algorithm

Chen,

Li,

Williams

et al. 2023

Journal of Food Science

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Blueberries are a nutritious and popular berry worldwide. The physical and chemical properties of blueberries constantly change through the cycle of the supply chain (from harvest to sale). The purpose of this study was to develop a rapid method for detecting the properties of packaged blueberries based on near‐infrared (NIR) spectroscopy. NIR was applied to quantitatively determine the soluble solid content (SSC) of polyethylene (PE)‐packaged blueberries. An orthogonal partial least squares discriminant analysis model was established to show the correlation between spectral data and the measured SSC. Multiplicative scattering correction, standard normal variable, Savitzky–Golay convolution first derivative, and normalization (Normalize) were used for spectra preprocessing. Uninformative variables elimination, competitive adaptive reweighted sampling, and iteratively retaining informative variables were jointly used for wavelength optimization. NIR‐based SSC prediction models for unpacked blueberries and PE‐packaged blueberries were developed using partial least squares (PLS). The prediction model for PE‐packaged samples (RP2= 0.876, root mean square error of prediction [RMSEP] = 0.632) had less precision than the model for unpacked samples (RP2= 0.953, RMSEP = 0.611). To reduce the effect of PE, the back propagation (BP) neural network and PLS were combined into the BP–PLS algorithm based on the residual learning algorithm. The model of BP–PLS (RP2= 0.947, RMSEP = 0.414) was successfully developed to improve the prediction accuracy of SSC for PE‐packaged blueberries. The results suggested a promising way of using the BP–PLS method in tandem with NIR for the rapid detection of the SSC of PE‐packaged blueberries.Practical ApplicationMost of the NIR‐based research used unpacked blueberries as samples, while the use of packaged blueberries would provide researchers with a better understanding of the crucial factors at different phases of the blueberry supply chain (from harvest to sale). To meet market demands and minimize losses, NIR spectroscopy has been proven to be a rapid and nondestructive method for the determination of the SSC of PE‐packaged blueberries. This study provides an effective method for monitoring the properties of blueberries in the entire supply chain.

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“…These calibrations also suffer failure when met with samples having unmodeled variability 17–19 . For fresh fruit, the model failure is commonly due to a high biological variability 20,21 . Biological variability can be related to different cultivars, the season of harvest, storage conditions and ripening stages of fruit 5,6,20,21 .…”

Section: Introductionmentioning

confidence: 99%

“…[17][18][19] For fresh fruit, the model failure is commonly due to a high biological variability. 20,21 Biological variability can be related to different cultivars, the season of harvest, storage conditions and ripening stages of fruit. 5,6,20,21 Hence, a natural solution to deal with this calibration failure problem is to measure a wide range of samples from different cultivars, seasons of harvest, storage conditions and ripening stages to calibrate global models.…”

mentioning

confidence: 99%

“…20,21 Biological variability can be related to different cultivars, the season of harvest, storage conditions and ripening stages of fruit. 5,6,20,21 Hence, a natural solution to deal with this calibration failure problem is to measure a wide range of samples from different cultivars, seasons of harvest, storage conditions and ripening stages to calibrate global models. 5,6 These global models are expected to work well in the face of the unseen variability that is expected to be met when predicting a new sample.…”

mentioning

confidence: 99%

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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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Self-adaptive models for predicting soluble solid content of blueberries with biological variability by using near-infrared spectroscopy and chemometrics

Cited by 31 publications

References 36 publications

Ripeness Prediction in Table Grape Cultivars by Using a Portable NIR Device

Ripeness Prediction in Table Grape Cultivars by Using a Portable NIR Device

Modeling of soluble solid content of PE‐packaged blueberries based on near‐infrared spectroscopy with back propagation neural network and partial least squares (BP–PLS) algorithm

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

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