Research

Jump to: Review

14 pages, 7219 KiB

Open AccessArticle

Preliminary Study of Pepper Types Based on Multielement Content Combined with Chemometrics

by Michaela Zeiner, Heidelore Fiedler, Iva Juranović Cindrić, Ivan Nemet, Doris Toma and Iva Habinovec

Foods 2023, 12(16), 3132; https://doi.org/10.3390/foods12163132 - 21 Aug 2023

Cited by 1 | Viewed by 891

Abstract

Different types of pepper (Piper nigrum L.) and cayenne pepper (Capsicum annuum L.) are widely used spices that exhibit therapeutic properties in addition to nutritional properties. In order to characterize these foods in further detail, the content of macro- (Ca, K, [...] Read more.

Different types of pepper (Piper nigrum L.) and cayenne pepper (Capsicum annuum L.) are widely used spices that exhibit therapeutic properties in addition to nutritional properties. In order to characterize these foods in further detail, the content of macro- (Ca, K, Mg, Na) and microelements (Ag, Al, As, Ba, Be, Bi, Cd, Co, Cr, Cu, Fe, Ga, Li, Mn, Mo, Ni, Pb, Rb, Se, Sr, Te, Tl, V and Zn) of four pepper types was determined via inductively coupled plasma mass spectrometry (ICP-MS) after microwave-assisted digestion using nitric acid and hydrogen peroxide. The obtained results were then evaluated using chemometric methods. The content of macroelements and microelements lies in the expected ranges for such spices but differs significantly between different types. The content of macro- and microelements is characteristic for pepper types originating from different plant species, but also based on further processing. Whilst green and black pepper are similar to each other, clearly diverse patterns are obtained for white pepper (different processing method) and cayenne pepper (different plant species). Full article

(This article belongs to the Special Issue Characterization and Quality Evaluation of Food and Beverages through Spectroscopy and Chemometrics)

► Show Figures

Figure 1

14 pages, 3338 KiB

Open AccessArticle

Study on Hyperspectral Monitoring Model of Total Flavonoids and Total Phenols in Tartary Buckwheat Grains

by Chenbo Yang, Lifang Song, Kunxi Wei, Chunrui Gao, Danli Wang, Meichen Feng, Meijun Zhang, Chao Wang, Lujie Xiao, Wude Yang and Xiaoyan Song

Foods 2023, 12(7), 1354; https://doi.org/10.3390/foods12071354 - 23 Mar 2023

Cited by 1 | Viewed by 1056

Abstract

Tartary buckwheat is a common functional food. Its grains are rich in flavonoids and phenols. The rapid measurement of flavonoids and phenols in buckwheat grains is of great significance in promoting the development of the buckwheat industry. This study, based on multiple scattering [...] Read more.

Tartary buckwheat is a common functional food. Its grains are rich in flavonoids and phenols. The rapid measurement of flavonoids and phenols in buckwheat grains is of great significance in promoting the development of the buckwheat industry. This study, based on multiple scattering correction (MSC), standardized normal variate (SNV), reciprocal logarithm (Lg), first-order derivative (FD), second-order derivative (SD), and fractional-order derivative (FOD) preprocessing spectra, constructed hyperspectral monitoring models of total flavonoids content and total phenols content in tartary buckwheat grains. The results showed that SNV, Lg, FD, SD, and FOD preprocessing had different effects on the original spectral reflectance and that FOD can also reflect the change process from the original spectrum to the integer-order derivative spectrum. Compared with the original spectrum, MSC, SNV, Lg, FD, and SD transformation spectra can improve the correlation between spectral data and total flavonoids and total phenols in varying degrees, while the correlation between FOD spectra of different orders and total flavonoids and total phenols in grains was different. The monitoring models of total flavonoids and total phenols in grains based on MSC, SNV, Lg, FD, and SD transformation spectra achieved the best accuracy under SD and FD transformation, respectively. Therefore, this study further constructed monitoring models of total flavonoids and total phenols content in grains based on the FOD spectrum and achieved the best accuracy under 1.6 and 0.6 order derivative preprocessing, respectively. The R²_c, RMSE_c, R²_v, RMSE_v, and RPD were 0.8731, 0.1332, 0.8384, 0.1448, and 2.4475 for the total flavonoids model, and 0.8296, 0.2025, 0.6535, 0.1740, and 1.6713 for the total phenols model. The model can realize the rapid measurement of total flavonoids content and total phenols content in tartary buckwheat grains, respectively. Full article

(This article belongs to the Special Issue Characterization and Quality Evaluation of Food and Beverages through Spectroscopy and Chemometrics)

► Show Figures

Figure 1

18 pages, 2692 KiB

Open AccessArticle

Non-Destructive Discrimination of Sunflower Seeds with Different Internal Mildew Grades by Fusion of Near-Infrared Diffuse Reflectance and Transmittance Spectra Combined with 1D-CNN

by Jie Liu, Shuang Fan, Weimin Cheng, Yang Yang, Xiaohong Li, Qi Wang, Binmei Liu, Zhuopin Xu and Yuejin Wu

Foods 2023, 12(2), 295; https://doi.org/10.3390/foods12020295 - 08 Jan 2023

Cited by 4 | Viewed by 1782

Abstract

Internally mildewed sunflower seeds, which cannot be recognized and discarded based on their appearance, pose a serious risk to human health. Thus, there is a need for a rapid non-destructive mildew grade discrimination method. Currently, few reports are available regarding this process. In [...] Read more.

Internally mildewed sunflower seeds, which cannot be recognized and discarded based on their appearance, pose a serious risk to human health. Thus, there is a need for a rapid non-destructive mildew grade discrimination method. Currently, few reports are available regarding this process. In this study, a method based on the combination of the near-infrared diffuse reflectance and near-infrared diffuse transmission (NIRr-NIRt) fusion spectra and a one-dimension convolutional neural network (1D-CNN) is proposed. The NIRr-NIRt fusion spectra can provide more complementary and comprehensive information, and therefore better discrimination accuracy, than a single spectrum. The first derivative (FD) preprocessing method could further improve the discrimination effect. By comparison against three conventional machine learning algorithms (artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN)), the 1D-CNN model based on the fusion spectra was found to perform the best. The mean prediction accuracy was 2.01%, 5.97%, and 10.55% higher than that of the ANN, SVM, and KNN models, respectively. These results indicate that the CNN model was able to precisely classify the mildew grades with a prediction accuracy of 97.60% and 94.04% for the training and test set, respectively. Thus, this study provides a non-destructive and rapid method for classifying the mildew grade of sunflower seeds with the potential to be applied in the quality control of sunflower seeds. Full article

(This article belongs to the Special Issue Characterization and Quality Evaluation of Food and Beverages through Spectroscopy and Chemometrics)

► Show Figures

Figure 1

16 pages, 1798 KiB

Open AccessArticle

Exploring the Analytical Complexities in Insect Powder Analysis Using Miniaturized NIR Spectroscopy

by Jordi Riu, Alba Vega, Ricard Boqué and Barbara Giussani

Foods 2022, 11(21), 3524; https://doi.org/10.3390/foods11213524 - 05 Nov 2022

Cited by 5 | Viewed by 1674

Abstract

Insects have been a food source for humans for millennia, and they are actively consumed in various parts of the world. This paper aims to ascertain the feasibility of portable near-infrared (NIR) spectroscopy as a reliable and fast candidate for the classification of [...] Read more.

Insects have been a food source for humans for millennia, and they are actively consumed in various parts of the world. This paper aims to ascertain the feasibility of portable near-infrared (NIR) spectroscopy as a reliable and fast candidate for the classification of insect powder samples and the prediction of their major components. Commercially-available insect powder samples were analyzed using two miniaturized NIR instruments. The samples were analyzed as they are and after grinding, to study the effect of the granulometry on the spectroscopic analyses. A homemade sample holder was designed and optimized for making reliable spectroscopic measurements. Classification was then performed using three classification strategies, and partial least squares (PLS) regression was used to predict the macronutrients. The results obtained confirmed that both spectroscopic sensors were able to classify insect powder samples and predict macronutrients with an adequate detection limit. Full article

(This article belongs to the Special Issue Characterization and Quality Evaluation of Food and Beverages through Spectroscopy and Chemometrics)

► Show Figures

Figure 1

13 pages, 330 KiB

Open AccessArticle

Effects of Different Processing Methods of Coffee Arabica on Colour, Acrylamide, Caffeine, Chlorogenic Acid, and Polyphenol Content

by Olga Cwiková, Tomas Komprda, Viera Šottníková, Zdeněk Svoboda, Jana Simonová, Jan Slováček and Miroslav Jůzl

Foods 2022, 11(20), 3295; https://doi.org/10.3390/foods11203295 - 21 Oct 2022

Cited by 6 | Viewed by 2034

Abstract

An effect of a processing method (dry and wet) and a degree of roasting (light, medium, and dark) of 15 coffee (Coffea arabica) samples on the content of caffeine, chlorogenic acid (CQA), total polyphenols (TPP), acrylamide (AA), and on the colour [...] Read more.

An effect of a processing method (dry and wet) and a degree of roasting (light, medium, and dark) of 15 coffee (Coffea arabica) samples on the content of caffeine, chlorogenic acid (CQA), total polyphenols (TPP), acrylamide (AA), and on the colour parameters L*, a*, and b* was evaluated. Neither processing nor roasting affected caffeine content (p > 0.05). The degree of roasting accounted for 46% and 72% of explained variability of the CQA content and AA content, respectively (p < 0.05). AA content was in the range from 250 (wet-processed, light-roasted samples) to 305 µg·kg⁻¹ (wet-processed, dark-roasted coffees), but the dark roasting only tended (p > 0.05) to increase AA content. Wet-processed, dry-roasted coffee had higher (p < 0.05) TPP content (48.5 mg·g⁻¹) than its dry-processed, dry-roasted counterpart (42.5 mg·g⁻¹); the method of processing accounted for 70% of explained variability of TPP. Both the method of processing and the degree of roasting affected the L*, a*, and b* values (p < 0.01), but the lower values (p < 0.05) of these parameters in the dark-roasted samples were found only within the wet processing. A negative correlation between the AA content and lightness (L*) was established (r = −0.39, p < 0.05). It was concluded that from the consumers’ viewpoint, the results of the present study indicate relatively small differences in quality parameters of coffee irrespective of the method of processing or degree of roasting. Full article

(This article belongs to the Special Issue Characterization and Quality Evaluation of Food and Beverages through Spectroscopy and Chemometrics)

18 pages, 2302 KiB

Open AccessArticle

NMR Metabolite Profiling in the Quality and Authentication Assessment of Greek Honey—Exploitation of STOCSY for Markers Identification

by Gabriela Belén Lemus Ringele, Stavros Beteinakis, Anastasia Papachristodoulou, Evangelos Axiotis, Emmanuel Mikros and Maria Halabalaki

Foods 2022, 11(18), 2853; https://doi.org/10.3390/foods11182853 - 15 Sep 2022

Cited by 6 | Viewed by 2399

Abstract

Honey is a natural, healthy commodity and is probably among the most complex foods produced by nature. It is the oldest recorded and certainly the only natural sweetener that can be used by humans without any further processing. Nowadays, the increase in honey’s [...] Read more.

Honey is a natural, healthy commodity and is probably among the most complex foods produced by nature. It is the oldest recorded and certainly the only natural sweetener that can be used by humans without any further processing. Nowadays, the increase in honey’s value, along with its growing list of healthy attributes, has made the present raw material a prime target for adulteration. In the current study, NMR-based metabolite profiling in combination with chemometrics was applied in the quality control of Greek honeys from northeastern Aegean islands. Moreover, statistical total correlation spectroscopy (STOCSY) was employed for the first time as a dereplication and structural elucidation tool in the honey biomarker identification process. A total of 10 compounds were successfully identified in honey total extracts via ¹H NMR spectroscopy. Compounds such as 5-(hydroxymethyl)furfural, methyl syringate, a mono-substituted glycerol derivative and 3-hydroxy-4-phenyl-2-butanone, among others, were identified as potential biomarkers related to the botanical and geographical origin of the samples. High-Resolution Mass Spectrometry (HRMS) was used as an additional verification tool on the identified compounds. Full article

(This article belongs to the Special Issue Characterization and Quality Evaluation of Food and Beverages through Spectroscopy and Chemometrics)

► Show Figures

Graphical abstract

13 pages, 1239 KiB

Open AccessArticle

Characterization and Classification of Direct and Commercial Strawberry Beverages Using Absorbance–Transmission and Fluorescence Excitation–Emission Matrix Technique

by Ewa Sikorska, Przemysław Nowak, Katarzyna Pawlak-Lemańska and Marek Sikorski

Foods 2022, 11(14), 2143; https://doi.org/10.3390/foods11142143 - 20 Jul 2022

Cited by 1 | Viewed by 1631

Abstract

The subject of this study was to characterize the absorption and fluorescence spectra of various types of strawberry beverages and to test the possibility of distinguishing between direct juices and pasteurized commercial products on the basis of their spectral properties. An absorbance and [...] Read more.

The subject of this study was to characterize the absorption and fluorescence spectra of various types of strawberry beverages and to test the possibility of distinguishing between direct juices and pasteurized commercial products on the basis of their spectral properties. An absorbance and transmission excitation–emission matrix (A-TEEM^TM) technique was used for the acquisition of spectra. The obtained spectra were analyzed using chemometric methods. The principal component analysis (PCA) revealed differences in both the absorption spectra and excitation–emission matrices (EEMs) of two groups of juices. The parallel factor analysis (PARAFAC) enabled the extraction and characterization of excitation and emission profiles and the relative contribution of four fluorescent components of juices, which were related to various groups of polyphenols and nonenzymatic browning products. Partial least squares–discriminant analysis (PLS-DA) models enabled 100% correct class assignment using the absorption spectra in the visible region, unfolded EEMs, and set of emission spectra with excitation at wavelengths of 275, 305, and 365 nm. The analysis of variable importance in projection (VIP) suggested that the polyphenols and nonenzymatic browning products may contribute significantly to the differentiation of commercial and direct juices. The results of the research may contribute to the development of fast methods to test the quality and authenticity of direct and processed strawberry juices. Full article

(This article belongs to the Special Issue Characterization and Quality Evaluation of Food and Beverages through Spectroscopy and Chemometrics)

► Show Figures

Graphical abstract

17 pages, 3288 KiB

Open AccessArticle

The Feasibility of Two Handheld Spectrometers for Meat Speciation Combined with Chemometric Methods and Its Application for Halal Certification

by Abolfazl Dashti, Judith Müller-Maatsch, Yannick Weesepoel, Hadi Parastar, Farzad Kobarfard, Bahram Daraei, Mohammad Hossein Shojaee AliAbadi and Hassan Yazdanpanah

Foods 2022, 11(1), 71; https://doi.org/10.3390/foods11010071 - 29 Dec 2021

Cited by 11 | Viewed by 2476

Abstract

Handheld visible-near-infrared (Vis-NIR) and near-infrared (NIR) spectroscopy can be cost-effective, rapid, non-destructive and transportable techniques for identifying meat species and may be valuable for enforcement authorities, retail and consumers. In this study, a handheld Vis-NIR (400–1000 nm) and a handheld NIR (900–1700 nm) [...] Read more.

Handheld visible-near-infrared (Vis-NIR) and near-infrared (NIR) spectroscopy can be cost-effective, rapid, non-destructive and transportable techniques for identifying meat species and may be valuable for enforcement authorities, retail and consumers. In this study, a handheld Vis-NIR (400–1000 nm) and a handheld NIR (900–1700 nm) spectrometer were applied to discriminate halal meat species from pork (halal certification), as well as speciation of intact and ground lamb, beef, chicken and pork (160 meat samples). Several types of class modeling multivariate approaches were applied. The presented one-class classification (OCC) approach, especially with the Vis-NIR sensor (95–100% correct classification rate), was found to be suitable for the application of halal from non-halal meat-species discrimination. In a discriminant approach, using the Vis-NIR data and support vector machine (SVM) classification, the four meat species tested could be classified with accuracies of 93.4% and 94.7% for ground and intact meat, respectively, while with partial least-squares discriminant analysis (PLS-DA), classification accuracies were 87.4% (ground) and 88.6% (intact). Using the NIR sensor, total accuracies of the SVM models were 88.2% and 81.5% for ground and intact meats, respectively, and PLS-DA classification accuracies were 88.3% (ground) and 80% (intact). We conclude that the Vis-NIR sensor was most successful in the halal certification (OCC approaches) and speciation (discriminant approaches) for both intact and ground meat using SVM. Full article

(This article belongs to the Special Issue Characterization and Quality Evaluation of Food and Beverages through Spectroscopy and Chemometrics)

► Show Figures

Graphical abstract

12 pages, 1860 KiB

Open AccessArticle

Near-Infrared Spectroscopy Applied to the Detection of Multiple Adulterants in Roasted and Ground Arabica Coffee

by Cinthia de Carvalho Couto, Otniel Freitas-Silva, Edna Maria Morais Oliveira, Clara Sousa and Susana Casal

Foods 2022, 11(1), 61; https://doi.org/10.3390/foods11010061 - 28 Dec 2021

Cited by 16 | Viewed by 2782

Abstract

Roasted coffee has been the target of increasingly complex adulterations. Sensitive, non-destructive, rapid and multicomponent techniques for their detection are sought after. This work proposes the detection of several common adulterants (corn, barley, soybean, rice, coffee husks and robusta coffee) in roasted ground [...] Read more.

Roasted coffee has been the target of increasingly complex adulterations. Sensitive, non-destructive, rapid and multicomponent techniques for their detection are sought after. This work proposes the detection of several common adulterants (corn, barley, soybean, rice, coffee husks and robusta coffee) in roasted ground arabica coffee (from different geographic regions), combining near-infrared (NIR) spectroscopy and chemometrics (Principal Component Analysis—PCA). Adulterated samples were composed of one to six adulterants, ranging from 0.25 to 80% (w/w). The results showed that NIR spectroscopy was able to discriminate pure arabica coffee samples from adulterated ones (for all the concentrations tested), including robusta coffees or coffee husks, and independently of being single or multiple adulterations. The identification of the adulterant in the sample was only feasible for single or double adulterations and in concentrations ≥10%. NIR spectroscopy also showed potential for the geographical discrimination of arabica coffees (South and Central America). Full article

(This article belongs to the Special Issue Characterization and Quality Evaluation of Food and Beverages through Spectroscopy and Chemometrics)

► Show Figures

Figure 1

Review

Jump to: Research

23 pages, 1351 KiB

Open AccessReview

Application of Near-Infrared Spectroscopy and Hyperspectral Imaging Combined with Machine Learning Algorithms for Quality Inspection of Grape: A Review

by Weixin Ye, Wei Xu, Tianying Yan, Jingkun Yan, Pan Gao and Chu Zhang

Foods 2023, 12(1), 132; https://doi.org/10.3390/foods12010132 - 27 Dec 2022

Cited by 9 | Viewed by 3169

Abstract

Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection [...] Read more.

Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection and safety assessment of agricultural products, which have great potential in recent years. The review summarized the applications and achievements of NIRS and HSI for the quality inspection of grapes for the last ten years. The review introduces basic principles, signal mode, data acquisition, analysis and processing of NIRS and HSI data. Qualitative and quantitative analysis were involved and compared, respectively, based on spectral features, image features and fusion data. The advantages, disadvantages and development trends of NIRS and HSI techniques in grape quality and safety inspection are summarized and discussed. The successful application of NIRS and HSI in grape quality inspection shows that many fruit inspection tasks could be assisted with NIRS and HSI. Full article

(This article belongs to the Special Issue Characterization and Quality Evaluation of Food and Beverages through Spectroscopy and Chemometrics)

► Show Figures

Figure 1

31 pages, 1196 KiB

Open AccessReview

Anthocyanins, Carotenoids and Chlorophylls in Edible Plant Leaves Unveiled by Tandem Mass Spectrometry

by Clara Sousa

Foods 2022, 11(13), 1924; https://doi.org/10.3390/foods11131924 - 28 Jun 2022

Cited by 9 | Viewed by 2856

Abstract

Natural pigments are a quite relevant group of molecules that are widely distributed in nature, possessing a significant role in our daily lives. Besides their colors, natural pigments are currently recognized as having relevant biological properties associated with health benefits, such as anti-tumor, [...] Read more.

Natural pigments are a quite relevant group of molecules that are widely distributed in nature, possessing a significant role in our daily lives. Besides their colors, natural pigments are currently recognized as having relevant biological properties associated with health benefits, such as anti-tumor, anti-atherogenicity, anti-aging and anti-inflammatory activities, among others. Some of these compounds are easily associated with specific fruits (such as blueberries with anthocyanins, red pitaya with betalain or tomato with lycopene), vegetables (carrots with carotenoids), plant leaves (chlorophylls in green leaves or carotenoids in yellow and red autumn leaves) and even the muscle tissue of vertebrates (such as myoglobin). Despite being less popular as natural pigment sources, edible plant leaves possess a high variety of chlorophylls, as well as a high variety of carotenoids and anthocyanins. The purpose of this review is to critically analyze the whole workflow employed to identify and quantify the most common natural pigments (anthocyanin, carotenoids and chlorophylls) in edible plant leaves using tandem mass spectrometry. Across the literature there, is a lack of consistency in the methods used to extract and analyze these compounds, and this review aims to surpass this issue. Additionally, mass spectrometry has stood out in the context of metabolomics, currently being a widely employed technique in this field. For the three pigments classes, the following steps will be scrutinized: (i) sample pre-preparation, including the solvents and extraction conditions; (ii) details of the chromatographic separation and mass spectrometry experiments (iii) pigment identification and quantification. Full article

(This article belongs to the Special Issue Characterization and Quality Evaluation of Food and Beverages through Spectroscopy and Chemometrics)

► Show Figures