Low-cost optical approach for noncontact predicting moisture content of apple slices during hot air drying

Mozaffari, Mansoureh; Mahmoudi, Asghar; Mollazade, Kaveh; Jamshidi, Bahareh

doi:10.1080/07373937.2016.1262394

Cited by 16 publications

(7 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, changes in spectral reflectance are caused by changes in absorption and scattering properties of fruits and vegetables during drying. These changes are influenced by physico-chemical transformations of chemical components, hardness, microstructure, and texture of the products which are induced by process conditions during convective drying (Mozaffari et al, 2016). According to Mozaffari et al (2016), changes in spectral profile of laser backscattering imaging during drying of apples were closely related to enzymatic oxidation, nonenzymatic browning as well as degradation of color and carotenoids.…”

Section: Analysis Of Spectral Reflectance and Wavelength Selection Ba...mentioning

confidence: 99%

“…These changes are influenced by physico-chemical transformations of chemical components, hardness, microstructure, and texture of the products which are induced by process conditions during convective drying (Mozaffari et al, 2016). According to Mozaffari et al (2016), changes in spectral profile of laser backscattering imaging during drying of apples were closely related to enzymatic oxidation, nonenzymatic browning as well as degradation of color and carotenoids. In the case of carrots, any noticeable changes in color can be correlated with degradation of total carotenoids and nonenzymatic browning (Koca et al, 2007).…”

Section: Analysis Of Spectral Reflectance and Wavelength Selection Ba...mentioning

confidence: 99%

See 1 more Smart Citation

Prediction of total carotenoids, color, and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique

Saleh

Kulig

Arefi

et al. 2022

Food Processing Preservation

View full text Add to dashboard Cite

The objective of this paper was to evaluate the performance of Partial Least Square Regression (PLSR) model and to assess the statistical agreement between two different measurement techniques, that is, Vis–NIR hyperspectral imaging (HSI) and standard laboratory methods for quality evaluation of dried carrots throughout the hot‐air drying process. Carrots at commercial maturity of 3.5 months after planting were harvested in two seasons (2017 and 2018) and dried in a convective hot air dryer at 50°C, 60°C, and 70°C. Quality measurements were examined at intervals of 30 minutes. PLSR was performed as a regression model to predict quality attributes in carrots, while Passing–Bablok and Deming regressions alongside Blant–Altman analysis were applied as method comparisons. Excellent prediction performance for moisture content was observed with high R2T and R2v at 0.92 and 0.90 with values of RMSET and RMSEv at 8.15% and 8.16%. Satisfactory prediction accuracies were observed for total carotenoids (R2v = 0.64 and RMSEv = 32.62) μg/g, L* (R2v = 0.68 and RMSEv = 32.62), a* (R2v = 0.69 and RMSEv = 1.18), and b* (R2v = 0.60 and RMSEv = 1.45). Selected wavelengths for total carotenoids, moisture content, L*, a*, and b* based on the highest score of VIP loadings were 531, 973, 531, 531, and 680 nm, respectively. An adequate agreement of Blant–Altman analysis between the two methods within the upper and lower limits of 95% confidence interval (CI) were obtained for total carotenoids from 95.68 μg/g to 82.34 μg/g, moisture content (25.18% to 22.93%), L* (2.88 to −3.30), a* (4.15 to 3.43), and b* (4.53 to −3.11) with mean differences at 6.67, 1.12, −0.21, 0.36, and 0.71, respectively. Good correlation coefficients (r) were also observed at 0.89, 0.91, 0.78, and 0.83 for moisture content, L*, a*, and b* with a moderate correlation of total carotenoids at 0.69. The results indicate the potential feasibility of using non‐invasive measurement of quality attributes using hyperspectral imaging during the drying of carrots. Novelty impact statement non‐invasive measurement using hyperspectral imaging for quality determination in carrots during convective drying demonstrated promising results. Multivariate analysis of Partial Least Square Regression showed a good modeling performance for quality prediction in dried carrots. A good statistical agreements between non‐invasive quality measurements using hyperspectral imaging and standard laboratory analysis were achieved by comparative analysis using Blant–Altman plot, Deming, and Passing–Bablok regression.

show abstract

Section: Analysis Of Spectral Reflectance and Wavelength Selection Ba...mentioning

confidence: 99%

Section: Analysis Of Spectral Reflectance and Wavelength Selection Ba...mentioning

confidence: 99%

Prediction of total carotenoids, color, and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique

Saleh

Kulig

Arefi

et al. 2022

Food Processing Preservation

View full text Add to dashboard Cite

show abstract

“…The measuring chamber (8b) is air-cooled with a set of fans and divided from the drying chamber (8a) by a glass plate to keep the imaging systems from heating up. Multi-spectral images, an RGB image, and the spectroradiometric reference measurements are acquired at~0 • with respect to the surface normal from the top of the chamber (10).…”

Section: Dryer and Drying Processmentioning

confidence: 99%

“…They highlight different contaminants that are critical for food safety and show how hyperspectral imaging can be used for early detection of contamination. Detailed work on the use of spectral and hyperspectral imaging in drying has been performed on tea [6], carrots [7], soy beans [8], and apples [9][10][11][12]. The use of hyperspectral imaging in the drying of mangoes has been studied by Pu and Sun, who investigated the changes in moisture content when drying mango slices in a microwave [13].…”

Section: Introductionmentioning

confidence: 99%

High End Quality Measuring in Mango Drying through Multi-Spectral Imaging Systems

et al. 2020

View full text Add to dashboard Cite

In modern fruit processing technology, non-destructive quality measuring techniques are sought for determining and controlling changes in the optical, structural, and chemical properties of the products. In this context, changes inside the product can be measured during processing. Especially for industrial use, fast, precise, but robust methods are particularly important to obtain high-quality products. In this work, a newly developed multi-spectral imaging system was implemented and adapted for drying processes. Further it was investigated if the system could be used to link changes in the surface spectral reflectance during mango drying with changes in moisture content and contents of chemical components. This was achieved by recovering the spectral reflectance from multi-spectral image data and comparing the spectral changes with changes of the total soluble solids (TSS), pH-value and the relative moisture content xwb of the products. In a first step, the camera was modified to be used in drying, then the changes in the spectra and quality criteria during mango drying were measured. For this, mango slices were dried at air temperatures of 40–80 °C and relative air humidities of 5%–30%. Samples were analyzed and pictures were taken with the multi-spectral imaging system. The quality criteria were then predicted from spectral data. It could be shown that the newly developed multi-spectral imaging system can be used for quality control in fruit drying. There are strong indications as well, that it can be employed for the prediction of chemical quality criteria of mangoes during drying. This way, quality changes can be monitored inline during the process using only one single measuring device.

show abstract

“…The MC of apple fruit is traditionally determined by oven drying, which is not only tedious and time-consuming but also destructive to samples. Therefore, in recent years, numerous rapid non-destructive detection methods have been proposed to determine the MC of apples (Dong & Guo, 2015;Guan, Liu, Huang, Kuang, & Liu, 2019;Mozaffari, Mahmoudi, Mollazade, & Jamshidi, 2016;Vesali et al, 2011;Li, Jin, Zhang, xiong, & Zhang, 2016;Zhang & Wang, 2008). For example, based on computed tomography (CT) technology, linear regression and a neural network were employed to predict the MC of Fuji apples, with results indicating the greater accuracy of the latter in predicting MC (Zhang & Wang, 2008).…”

mentioning

confidence: 99%

Building kinetic models to determine moisture content in apples and predicting shelf life based on spectroscopy

Liu

Qiao

Hou

et al. 2021

J Food Process Engineering

View full text Add to dashboard Cite

Moisture content (MC) directly affects the storage of apples. This study explored the prediction of MC and the shelf life of apples stored at room temperature. Using wavelengths selected by the competitive adaptive reweighted sampling algorithm and chemical kinetic reactions, a quantitative prediction model based on optical fiber spectroscopy and a kinetic model were established to determine the MC of apples stored at room temperature. The shelf life of apples was predicted based on the developed quantitative prediction and kinetic models. The determination coefficients of the calibration and prediction of the quantitative prediction model were determined as .9810 and .9686, with root mean square errors of .0020 and .0022, respectively, and a residual predictive deviation of 6.55. In addition, the correlation coefficient of the kinetic model was derived as .9957, with a root mean square error of .0011. Results demonstrate that when the MC of the fresh apples exceeds 85% and the initial MC of apples stored at room temperature equals 87%, the freshness preservation time of the apples should not exceed 28 days. The proposed quantitative and kinetic models are able to rapidly and accurately monitor the MC of apples, and the real-time prediction of apple shelf life can be realized according to the difference in the spectral information pre-and post-storage at room temperature. Practical ApplicationThis study provides a method for the determination of moisture content (MC) in apples and the prediction of shelf life based on kinetic models. According to the proposed method, enterprises can not only quickly and accurately detect the MC of apples to determine whether they are fresh, but can also easily predict the shelf life of apples to determine whether they are safe. | INTRODUCTIONApple is one of the most popular and important fruits worldwide. In particular, apple is favored by consumers because of its antioxidant, anti-aging, and immunity-enhancing functions (Briones-

show abstract

Low-cost optical approach for noncontact predicting moisture content of apple slices during hot air drying

Cited by 16 publications

References 45 publications

Prediction of total carotenoids, color, and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique

Prediction of total carotenoids, color, and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique

High End Quality Measuring in Mango Drying through Multi-Spectral Imaging Systems

Building kinetic models to determine moisture content in apples and predicting shelf life based on spectroscopy

Contact Info

Product

Resources

About