Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging

Leiva-Valenzuela, Gabriel A.; Lu, Renfu; Aguilera, José Miguel

doi:10.1016/j.jfoodeng.2012.10.001

Cited by 201 publications

(89 citation statements)

References 23 publications

(27 reference statements)

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“…In reflectance mode (Figure 3a), the light source and the detector are placed above the sample and the measured light is diffusely reflected from the illuminated area of the sample. This sensing mode is easy to implement and has been widely used to detect quality attributes, both external and internal, of food products [16,[32][33][34]. However, in reflectance mode, light has limited penetration in horticultural and food products, and hence this sensing mode may not be quite effective for detecting internal quality.…”

Section: Commen Sensing Modesmentioning

confidence: 99%

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Huang

2017

Applied Sciences

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New, non-destructive sensing techniques for fast and more effective quality assessment of fruits and vegetables are needed to meet the ever-increasing consumer demand for better, more consistent and safer food products. Over the past 15 years, hyperspectral imaging has emerged as a new generation of sensing technology for non-destructive food quality and safety evaluation, because it integrates the major features of imaging and spectroscopy, thus enabling the acquisition of both spectral and spatial information from an object simultaneously. This paper first provides a brief overview of hyperspectral imaging configurations and common sensing modes used for food quality and safety evaluation. The paper is, however, focused on the three innovative hyperspectral imaging-based techniques or sensing platforms, i.e., spectral scattering, integrated reflectance and transmittance, and spatially-resolved spectroscopy, which have been developed in our laboratory for property and quality evaluation of fruits, vegetables and other food products. The basic principle and instrumentation of each technique are described, followed by the mathematical methods for processing and extracting critical information from the acquired data. Applications of these techniques for property and quality evaluation of fruits and vegetables are then presented. Finally, concluding remarks are given on future research needs to move forward these hyperspectral imaging techniques.

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Section: Commen Sensing Modesmentioning

confidence: 99%

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Huang

2017

Applied Sciences

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“…Nowadays, the visual analysis of skin blue color is recognized as one of the most important maturity index used for blueberry fruits (Leiva-Valenzuela et al, 2013), which can result in inadequate homogeneity at harvest. The blue color in blueberries skin is a highly complex attribute, which is mainly function of the accumulation of anthocyanins (Ehlenfeldt and Prior, 2001), the distribution of individual anthocyanins, and the formation of metal complexes of anthocyanins (Harborne, 1976).…”

Section: Several Agronomic and Physiological Factors Canmentioning

confidence: 99%

“…The blue color in blueberries skin is a highly complex attribute, which is mainly function of the accumulation of anthocyanins (Ehlenfeldt and Prior, 2001), the distribution of individual anthocyanins, and the formation of metal complexes of anthocyanins (Harborne, 1976). Nonetheless, the human eye is not capable to sort accurately mature fruit deriving in a high heterogeneity of harvested fruits (Ziosi et al, 2008;Leiva-Valenzuela et al, 2013).…”

Section: Several Agronomic and Physiological Factors Canmentioning

confidence: 99%

Non-destructive Assessment of Highbush Blueberry Fruit Maturity Parameters and Anthocyanins by Using a Visible/Near Infrared (vis/NIR) Spectroscopy Device: A Preliminary Approach

Ribera-Fonseca

Noferini²,

Rombolà

2016

J. Soil Sci. Plant Nutr.

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We evaluate the use of a portable and non-invasive technology based on visible and near infrared (vis/NIR) spectroscopy (Cherry-Meter) for monitoring fruit maturity parameters and anthocyanins in two highbush blueberry (Vaccinium corymbosum L.) cultivars (Misty and Sharpblue). This device measures an Index of Absorbance Difference (I AD ), which showed high correlations with fruit quality parameters in other fruit crops. We found positive but differential correlations between I AD values and fruit anthocyanins, which was higher for Misty (r = 0.970, p≤0.01) than for Sharpblue (r = 0.714, p>0.05). Interestingly, Cherry-Meter measurements were also correlated with solid soluble concentrations (r = 0.685, p≤0.01) and fruit firmness (r = -0.714, p≤0.01), but only in Sharpblue. For both cultivars, I AD values were also significantly (p≤0.01) related with fruit FW (0.447 for Sharpblue and 0.559 for Misty). The High Performance Liquid Chromatography (HPLC) data indicated that I AD variations were associated with significant changes in single berry anthocyanidins levels. These findings are the first approach, highlighting the potentialities of Cherry-Meter for the non-destructive assessment of fruit maturity and anthocyanidin profile in blueberries.

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“…Huang and Lu [23] carried out apple mealiness detection using hyperspectral scattering imaging. Furthermore, quality attributes like soluble solids content, pH, and firmness can be successfully predicted by hyperspectral imaging [6,[24][25][26]. The technique, however, has so far been largely limited to laboratory or benchtop applications because of the cost, huge volume of data, and computing requirements.…”

Section: Introductionmentioning

confidence: 99%

Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm

Song

Sun

et al. 2016

Applied Sciences

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Radishes with black hearts will lose edible value and cause food safety problems, so it is important to detect and remove the defective ones before processing and consumption. A hyperspectral transmittance imaging system with 420 wavelengths was developed to capture images from white radishes. A successive-projections algorithm (SPA) was applied with 10 wavelengths selected to distinguish defective radishes with black hearts from normal samples. Pearson linear correlation coefficients were calculated to further refine the set of wavelengths with 4 wavelengths determined. Four chemometric classifiers were developed for classification of normal and defective radishes, using 420, 10 and 4 wavelengths as input variables. The overall classifying accuracy based on the four classifiers were 95.6%-100%. The highest classification with 100% was obtained with a back propagation artificial neural network (BPANN) for both calibration and prediction using 420 and 10 wavelengths. Overall accuracies of 98.4% and 97.8% were obtained for calibration and prediction, respectively, with Fisher's linear discriminant analysis (FLDA) based on 4 wavelengths, and was better than the other three classifiers. This indicated that the developed hyperspectral transmittance imaging was suitable for black heart detection in white radishes with the optimal wavelengths, which has potential for fast on-line discrimination before food processing or reaching storage shelves.

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Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging

Cited by 201 publications

References 23 publications

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Non-destructive Assessment of Highbush Blueberry Fruit Maturity Parameters and Anthocyanins by Using a Visible/Near Infrared (vis/NIR) Spectroscopy Device: A Preliminary Approach

Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm

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