Special Issue "Non-destructive Sensors in Food Authentication"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (30 November 2019).

Special Issue Editor

Special Issue Information

Dear Colleagues,

Compared with other conventional analytical methods, the use of non-destructive sensors (e.g., infrared, electronic noses, Raman, and hyperspectral) confers the food industry with a wide range of tools to target food authenticity issues. Many available sensor systems are those based on the application of hyperspectral imaging; visible, near-, and mid-infrared; fluorescence; Raman spectroscopy; electronic noses; and tongues that can be applied to a vast range of samples and applications. In recent years, technological advances have made non-destructive sensors available for both research and industry applications. Consequently, there is growing interest in the non-destructive evaluation of foods and agricultural products targeting authentication issues (e.g., geographical origin, provenance).

This upcoming Special Issue of Applied Sciences will focus on recent developments and applications of the use of non-destructive sensors (e.g., infrared, electronic noses, Raman, and hyperspectral) to target food authentication issues in agricultural commodities and foods. Advances in the hardware and instrumentation, chemometrics, methodology, and practical implementation of these technologies will be considered. We would like to invite you to submit or recommend original research papers for the “Non-Destructive Sensors for Food Authentication” Special Issue.

Assoc. Prof. Daniel Cozzolino
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Sensors
  • Authenticity
  • Food
  • Non-destructive…

Published Papers (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Open AccessArticle
Visible/near Infrared Reflection Spectrometer and Electronic Nose Data Fusion as an Accuracy Improvement Method for Portable Total Soluble Solid Content Detection of Orange
Appl. Sci. 2019, 9(18), 3761; https://doi.org/10.3390/app9183761 - 09 Sep 2019
Cited by 4 | Viewed by 658
Abstract
The visible/near infrared (VIS/NIR) spectrometer and electronic nose (E-nose) are two commonly used portable and nondestructive detection apparatuses which have a promising application for the quick acquisition of fruit’s internal quality in both the orchard and market. However, the accuracy of these instruments [...] Read more.
The visible/near infrared (VIS/NIR) spectrometer and electronic nose (E-nose) are two commonly used portable and nondestructive detection apparatuses which have a promising application for the quick acquisition of fruit’s internal quality in both the orchard and market. However, the accuracy of these instruments is sometimes unsatisfactory, especially for thick peeled fruit like the ‘Aiyuan 38’ orange, which was investigated in this research. The objective of this research was to find a method to improve the accuracy for the detection of an orange’s total soluble solid content (TSS) using a VIS/NIR spectrometer and E-nose. Different spectrum detection positions and conventional feature extraction methods are compared to get the optimal data fusion parameters. The detection model was then built up based on the obtained fusion data under the optimal parameters. Partial least squares regression (PLSR) and mutual information theory (MIT) were applied for feature extraction, and PLSR and principal component analysis (PCA)-back propagation neural network (BPNN) were applied for modeling and detection. PLSR results showed that the sampling reflection spectrum at the position of the calyx results in a better orange TSS detection than other sampling positions. For VIS/NIR reflection spectrum feature extraction, PLSR and MIT results showed that the optimal data process + feature extraction method is Savitzky-Golay + 763 features, when their mutual information values between the feature and TSS value were larger than 0.74. For E-nose feature extraction, PLSR and MIT results showed that the combined feature (combination of 75 s value, average value, average of differential value, integral value, and maximum value) is the optimal feature extraction method, and all features are retained for modeling. The PLSR detection ability of orange TSS based on fusion data is better than the single detection method, with the detection ability of the single detection methods being unsatisfactory. PCA-BPNN has better orange TSS detection ability than PLSR. The R2, RMSE, and slope from the calibration set for PCA-BPNN detection were 0.9695, 0.1895, and 0.9665, respectively, and from the validation set for PCA-BPNN detection were 0.8872, 0.4709, and 1.0871, respectively, indicating that this method can detect orange TSS efficiently. Full article
(This article belongs to the Special Issue Non-destructive Sensors in Food Authentication)
Show Figures

Figure 1

Open AccessFeature PaperArticle
Influence of the Scanning Temperature on the Classification of Whisky Samples Analysed by UV-VIS Spectroscopy
Appl. Sci. 2019, 9(16), 3254; https://doi.org/10.3390/app9163254 - 09 Aug 2019
Cited by 3 | Viewed by 1093
Abstract
The definition of the optimal temperature and its effects (either increasing or variations) during analysis of alcoholic beverages are of importance to develop protocols based in spectroscopy. Although several reports have been published on the use of spectroscopy combined with chemometrics to classify [...] Read more.
The definition of the optimal temperature and its effects (either increasing or variations) during analysis of alcoholic beverages are of importance to develop protocols based in spectroscopy. Although several reports have been published on the use of spectroscopy combined with chemometrics to classify and authenticate alcoholic beverages (e.g., wine, tequila, whisky), few reports deal with issues related with the spectra collection (e.g., temperature, path length) and its effect on the classification performances. The objective of this study was to evaluate the effect of increasing temperature on both the UV-VIS spectra of whisky and on the classification results of the samples according to country of origin. Whisky samples from different commercial labels were analysed at different temperatures (25, 35, 45, 55 °C) using a UV-VIS instrument (Agilent, Cary 3500). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) models based in cross validation were used to classify whisky samples according to scanning temperature and origin. The results of this study indicated that temperature did not affect the classification of whisky samples according to country of origin. Overall, well defined protocols need to be defined for routine use of these methods in research and by the industry. Full article
(This article belongs to the Special Issue Non-destructive Sensors in Food Authentication)
Show Figures

Figure 1

Open AccessArticle
A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue
Appl. Sci. 2019, 9(12), 2518; https://doi.org/10.3390/app9122518 - 20 Jun 2019
Cited by 4 | Viewed by 962
Abstract
Feature extraction is a key part of the electronic tongue system. Almost all of the existing features extraction methods are “hand-crafted”, which are difficult in features selection and poor in stability. The lack of automatic, efficient and accurate features extraction methods has limited [...] Read more.
Feature extraction is a key part of the electronic tongue system. Almost all of the existing features extraction methods are “hand-crafted”, which are difficult in features selection and poor in stability. The lack of automatic, efficient and accurate features extraction methods has limited the application and development of electronic tongue systems. In this work, a convolutional neural network-based auto features extraction strategy (CNN-AFE) in an electronic tongue (e-tongue) system for tea classification was proposed. First, the sensor response of the e-tongue was converted to time-frequency maps by short-time Fourier transform (STFT). Second, features were extracted by convolutional neural network (CNN) with time-frequency maps as input. Finally, the features extraction and classification results were carried out under a general shallow CNN architecture. To evaluate the performance of the proposed strategy, experiments were held on a tea database containing 5100 samples for five kinds of tea. Compared with other features extraction methods including features of raw response, peak-inflection point, discrete cosine transform (DCT), discrete wavelet transform (DWT) and singular value decomposition (SVD), the proposed model showed superior performance. Nearly 99.9% classification accuracy was obtained and the proposed method is an approximate end-to-end features extraction and pattern recognition model, which reduces manual operation and improves efficiency. Full article
(This article belongs to the Special Issue Non-destructive Sensors in Food Authentication)
Show Figures

Figure 1

Open AccessArticle
Nondestructive Evaluation of Apple Fruit Quality by Frequency-Domain Diffuse Reflectance Spectroscopy: Variations in Apple Skin and Flesh
Appl. Sci. 2019, 9(11), 2355; https://doi.org/10.3390/app9112355 - 08 Jun 2019
Cited by 1 | Viewed by 909
Abstract
The optical properties of fruits, such as light absorption and scattering characteristics, change with biochemical activities during storage. Diffuse reflectance spectroscopy (DRS) systems have been widely applied for noninvasively observing biological tissues. In this study, we used a frequency-domain DRS system to measure [...] Read more.
The optical properties of fruits, such as light absorption and scattering characteristics, change with biochemical activities during storage. Diffuse reflectance spectroscopy (DRS) systems have been widely applied for noninvasively observing biological tissues. In this study, we used a frequency-domain DRS system to measure the optical properties of apples. Results showed that variations in the chlorophyll, water, and flesh-texture of apples could be noninvasively monitored over time. We also observed substantial differences in the absorption and reduced scattering coefficients between injured and normal apples. The DRS techniques could be used for apple grading, and, by extension, for monitoring the quality of other fruits. Full article
(This article belongs to the Special Issue Non-destructive Sensors in Food Authentication)
Show Figures

Figure 1

Open AccessArticle
Evaluation of Yogurt Quality during Storage by Fluorescence Spectroscopy
Appl. Sci. 2019, 9(1), 131; https://doi.org/10.3390/app9010131 - 02 Jan 2019
Cited by 2 | Viewed by 1311
Abstract
The physico-chemical parameters including pH and viscosity, and the fluorescence signal induced by fluorescent compounds presenting in yogurts such as riboflavin and porphyrin were measured during one week’s storage at room temperature when five brands of yogurt samples were exposed to ambient air. [...] Read more.
The physico-chemical parameters including pH and viscosity, and the fluorescence signal induced by fluorescent compounds presenting in yogurts such as riboflavin and porphyrin were measured during one week’s storage at room temperature when five brands of yogurt samples were exposed to ambient air. The fluorescence spectra of yogurt showed four evident emission peaks, 525 nm, 633 nm, 661 nm, and 672 nm. To quantitatively investigate the quality of yogurt during deteriorating, a calculating method of the average rate of change (ARC) was proposed to study the relative change of fluorescence intensity in the spectral range of 600 to 750 nm associated with porphyrin and chlorin compounds. During the storage, the time evolution of two ARC, pH value, and viscosity were regular. Moreover, the ARC showed a good linear relationship with pH value and viscosity of yogurt. Further, multiple linear regression (MLR) models using two ARC as independent variables were developed to verify the dependence of fluorescence signal with pH value and viscosity, which showed a good linear relationship with an R-square of more than 85% for each class of yogurt. The results demonstrate that fluorescence spectra have a great potential to predict the quality of yogurt. Full article
(This article belongs to the Special Issue Non-destructive Sensors in Food Authentication)
Show Figures

Figure 1

Review

Jump to: Research

Open AccessFeature PaperReview
The Brewing Industry and the Opportunities for Real-Time Quality Analysis Using Infrared Spectroscopy
Appl. Sci. 2020, 10(2), 616; https://doi.org/10.3390/app10020616 - 15 Jan 2020
Cited by 1 | Viewed by 994
Abstract
Brewing is an ancient process which started in the middle east over 10,000 years ago. The style of beer varies across the globe but modern brewing is very much the same regardless of the style. While there are thousands of compounds in beer, [...] Read more.
Brewing is an ancient process which started in the middle east over 10,000 years ago. The style of beer varies across the globe but modern brewing is very much the same regardless of the style. While there are thousands of compounds in beer, current methods of analysis rely mostly on the content of only several important processing parameters such as gravity, bitterness, or alcohol. Near infrared and mid infrared spectroscopy offer opportunities to predict dozens to hundreds of compounds simultaneously at different stages of the brewing process. Importantly, this is an opportunity to move deeper into quality through measuring wort and beer composition, rather than just content. This includes measuring individual sugars and amino acids prior to fermentation, rather than total °Plato or free amino acids content. Portable devices and in-line probes, coupled with more complex algorithms can provide real time measurements, allowing brewers more control of the process, resulting in more consistent quality, reduced production costs and greater confidence for the future. Full article
(This article belongs to the Special Issue Non-destructive Sensors in Food Authentication)
Show Figures

Graphical abstract

Back to TopTop