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Da Silva Ferreira MV, Barbosa JL, Kamruzzaman M, Barbin DF. Correction: Low-cost electronic-nose (LC-e-nose) systems for the evaluation of plantation and fruit crops: recent advances and future trends. Anal Methods 2024; 16:959. [PMID: 38287912 DOI: 10.1039/d3ay90149a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Correction for 'Low-cost electronic-nose (LC-e-nose) systems for the evaluation of plantation and fruit crops: recent advances and future trends' by Marcus Vinicius da Silva Ferreira et al., Anal. Methods, 2023, https://doi.org/10.1039/D3AY01192E.
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Affiliation(s)
- Marcus Vinicius Da Silva Ferreira
- Universidade Federal Rural do Rio de Janeiro (UFRRJ), Departamento de Tecnologia de Alimentos, Seropédica 23890-000, Rio de Janeiro, Brazil.
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Jose Lucena Barbosa
- Universidade Federal Rural do Rio de Janeiro (UFRRJ), Departamento de Tecnologia de Alimentos, Seropédica 23890-000, Rio de Janeiro, Brazil.
| | - Mohammed Kamruzzaman
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Douglas Fernandes Barbin
- Department of Food Engineering and Technology, School of Food Engineering, University of Campinas, Campinas, SP, Brazil
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Vinicius da Silva Ferreira M, Barbosa JL, Kamruzzaman M, Barbin DF. Low-cost electronic-nose (LC-e-nose) systems for the evaluation of plantation and fruit crops: recent advances and future trends. Anal Methods 2023; 15:6120-6138. [PMID: 37937362 DOI: 10.1039/d3ay01192e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
An electronic nose (e-nose) is a device designed to recognize and classify odors. The equipment is built around a series of sensors that detect the presence of odors, especially volatile organic compounds (VOCs), and generate an electric signal (voltage), known as e-nose data, which contains chemical information. In the food business, the use of e-noses for analyses and quality control of fruits and plantation crops has increased in recent years. Their use is particularly relevant due to the lack of non-invasive and inexpensive methods to detect VOCs in crops. However, the majority of reports in the literature involve commercial e-noses, with only a few studies addressing low-cost e-nose (LC-e-nose) devices or providing a data-oriented description to assist researchers in choosing their setup and appropriate statistical methods to analyze crop data. Therefore, the objective of this study is to discuss the hardware of the two most common e-nose sensors: electrochemical (EC) sensors and metal oxide sensors (MOSs), as well as a critical review of the literature reporting MOS-based low-cost e-nose devices used for investigating plantations and fruit crops, including the main features of such devices. Miniaturization of equipment from lab-scale to portable and convenient gear, allowing producers to take it into the field, as shown in many appraised systems, is one of the future advancements in this area. By utilizing the low-cost designs provided in this review, researchers can develop their own devices based on practical demands such as quality control and compare results with those reported in the literature. Overall, this review thoroughly discusses the applications of low-cost e-noses based on MOSs for fruits, tea, and coffee, as well as the key features of their equipment (i.e., advantages and disadvantages) based on their technical parameters (i.e., electronic and physical parts). As a final remark, LC-e-nose technology deserves significant attention as it has the potential to be a valuable quality control tool for emerging countries.
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Affiliation(s)
- Marcus Vinicius da Silva Ferreira
- Universidade Federal Rural do Rio de Janeiro (UFRRJ), Departamento de Tecnologia de Alimentos, Seropédica 23890-000, Rio de Janeiro, Brazil.
- Department of Agriculture and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Jose Lucena Barbosa
- Universidade Federal Rural do Rio de Janeiro (UFRRJ), Departamento de Tecnologia de Alimentos, Seropédica 23890-000, Rio de Janeiro, Brazil.
| | - Mohammed Kamruzzaman
- Department of Agriculture and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Douglas Fernandes Barbin
- Department of Food Engineering and Technology, School of Food Engineering, University of Campinas, Campinas, SP, Brazil
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Bordignon JCS, Badaró AT, Barbin DF, Mariutti LRB, Netto FM. Oxidation of whey protein isolate after thermal convection and microwave heating and freeze-drying: Correlation among physicochemical and NIR spectroscopy analyses. Heliyon 2023; 9:e17981. [PMID: 37519701 PMCID: PMC10373659 DOI: 10.1016/j.heliyon.2023.e17981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 06/27/2023] [Accepted: 07/04/2023] [Indexed: 08/01/2023] Open
Abstract
This study investigated the oxidative susceptibility of whey protein isolate (WPI) dispersions treated by microwave or thermal convection before freeze-drying. WPI (20 mg protein/mL) in distilled water (DW) was heated at 63 ± 2 °C for 30 min by microwave (WPI-MW) or convection heating (WPI-CH) and freeze-dried. Untreated WPI (WPI-C), WPI solubilized in DW and freeze-dried (WPI-FD), and WPI solubilized in DW, heated at 98 ± 2 °C for 2 min and freeze-dried (WPI-B) were also evaluated. Structural changes (turbidity, ζ potential, SDS-PAGE, and near-infrared spectroscopy (NIR)) and protein oxidation (dityrosine, protein carbonylation, and SH groups) were investigated. WPI-FD showed alterations compared to WPI-C, mainly concerning carbonyl groups. Microwave heating increased carbonyl groups and dityrosine formation compared to conventional heating. NIR spectrum indicated changes related to the formation of carbonyl groups and PCA analysis allowed us to distinguish the samples according to carbonyl group content. The results suggest that NIR may contribute to monitoring oxidative changes in proteins resulting from processing.
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Medeiros MLDS, Freitas Lima A, Correia Gonçalves M, Teixeira Godoy H, Fernandes Barbin D. Portable near-infrared (NIR) spectrometer and chemometrics for rapid identification of butter cheese adulteration. Food Chem 2023; 425:136461. [PMID: 37285626 DOI: 10.1016/j.foodchem.2023.136461] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/22/2023] [Accepted: 05/16/2023] [Indexed: 06/09/2023]
Abstract
Artisanal cheeses are highly valued around the world for their distinct sensory characteristics, thus being prone to adulteration by substituting authentic material for cheaper products, such as vegetable oil. In this work, we developed a method based on a portable NIR spectrometer as a non-destructive and low-cost alternative to identify adulteration in butter cheese. Dataset consisted of authentic and intentionally adulterated cheeses in the laboratory and commercial cheeses, which were identified as authentic and adulterated with vegetable oil after analysis of the fatty acid profile. PLS-DA classification models identified adulterated samples with an accuracy of 94.44%. PLS prediction models showed excellent performance (RPD > 3.0) to predict the adulterant level. These results demonstrate that NIR spectra can be used to identify the replacement of authentic fat by soybean oil in butter cheese and that the developed models can be used to identify adulteration in external samples with good performance.
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Affiliation(s)
| | - Adriano Freitas Lima
- Department of Food Engineering, School of Food Engineering, University of Campinas, Campinas, SP, Brazil
| | - Mônica Correia Gonçalves
- Agrifood Science and Technology Center, Federal University of Campina Grande, Pombal, PB, Brazil
| | - Helena Teixeira Godoy
- Department of Food Engineering, School of Food Engineering, University of Campinas, Campinas, SP, Brazil.
| | - Douglas Fernandes Barbin
- Department of Food Engineering, School of Food Engineering, University of Campinas, Campinas, SP, Brazil.
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Oliveira MM, Badaró AT, Esquerre CA, Kamruzzaman M, Barbin DF. Handheld and benchtop vis/NIR spectrometer combined with PLS regression for fast prediction of cocoa shell in cocoa powder. Spectrochim Acta A Mol Biomol Spectrosc 2023; 298:122807. [PMID: 37148660 DOI: 10.1016/j.saa.2023.122807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/11/2023] [Accepted: 04/28/2023] [Indexed: 05/08/2023]
Abstract
The fermented and dried cocoa beans are peeled, either before or after the roasting process, as peeled nibs are used for chocolate production, and shell content in cocoa powders may result from economically motivated adulteration (EMA), cross-contamination or misfits in equipment in the peeling process. The performance of this process is carefully evaluated, as values above 5% (w/w) of cocoa shell can directly affect the sensory quality of cocoa products. In this study chemometric methods were applied to near-infrared (NIR) spectra from a handheld (900-1700 nm) and a benchtop (400-1700 nm) spectrometers to predict cocoa shell content in cocoa powders. A total of 132 binary mixtures of cocoa powders with cocoa shell were prepared at several proportions (0 to 10% w/w). Partial least squares regression (PLSR) was used to develop the calibration models and different spectral preprocessing were investigated to improve the predictive performance of the models. The ensemble Monte Carlo variable selection (EMCVS) method was used to select the most informative spectral variables. Based on the results obtained with both benchtop (R2P = 0.939, RMSEP = 0.687% and RPDP = 4.14) and handheld (R2P = 0.876, RMSEP = 1.04% and RPDP = 2.82) spectrometers, NIR spectroscopy combined with the EMCVS method proved to be a highly accurate and reliable tool for predicting cocoa shell in cocoa powder. Even with a lower predictive performance than the benchtop spectrometer, the handheld spectrometer has potential to specify whether the amount of cocoa shell present in cocoa powders is in accordance with the Codex Alimentarius specifications.
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Affiliation(s)
- M M Oliveira
- Department of Food Engineering and Technology, School of Food Engineering, University of Campinas, Campinas, SP, Brazil; Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - A T Badaró
- Department of Food Engineering and Technology, School of Food Engineering, University of Campinas, Campinas, SP, Brazil
| | - C A Esquerre
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - M Kamruzzaman
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - D F Barbin
- Department of Food Engineering and Technology, School of Food Engineering, University of Campinas, Campinas, SP, Brazil.
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da Silva Medeiros ML, Brasil YL, Cruz-Tirado LJP, Lima AF, Godoy HT, Barbin DF. Portable NIR spectrometer and chemometric tools for predicting quality attributes and adulteration levels in butteroil. Food Control 2023. [DOI: 10.1016/j.foodcont.2022.109349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Cruz-Tirado JP, Lopes de França PR, Fernandes Barbin D. Chia (Salvia hispanica) seeds degradation studied by fuzzy-c mean (FCM) and hyperspectral imaging and chemometrics - fatty acids quantification. Sci agropecu 2022. [DOI: 10.17268/sci.agropecu.2022.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Chia seeds are nutritious food because they have a high content of protein, polyunsaturated fatty acids (omega-3 and omega-6) and phenolic compounds. During storage, fatty acids are degraded, by oxidative and hydrolytic reactions, forming free fatty acids (FFA). In this work, we used Near Infrared Hyperspectral Imaging (NIR- HSI) and chemometrics to predict FFA acid value and fatty acids concentrations in chia seeds during storage. First, we explore the hyperspectral images by Fuzzy c-means (FCM), where it is possible to observe as chemical compounds are formed or degraded during storage. Second, PLSR models were developed to predict FFA value and fatty acids concentration. RPD values reached values higher then 2.0, indicating a good ability to estimate these chemical compounds, especially polyunsaturated fatty acids omega-3 and omega-6. Finally, NIR-hyperspectral imaging coupled with chemometrics allowed us to show the chemical degradation process of chia seeds during storage, mainly associated with polyunsaturated fatty acids degradation. Besides NIR-HSI showed to be a powerful technique to quantify the main fatty acids with high accuracy.
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Cruz-Tirado J, Amigo JM, Barbin DF. Determination of protein content in single black fly soldier (Hermetia illucens L.) larvae by near infrared hyperspectral imaging (NIR-HSI) and chemometrics. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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da Silva Medeiros ML, Cruz-Tirado J, Lima AF, de Souza Netto JM, Ribeiro APB, Bassegio D, Godoy HT, Barbin DF. Assessment oil composition and species discrimination of Brassicas seeds based on hyperspectral imaging and portable near infrared (NIR) spectroscopy tools and chemometrics. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104403] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Cruz-Tirado J, Amigo JM, Barbin DF, Kucheryavskiy S. Data reduction by randomization subsampling for the study of large hyperspectral datasets. Anal Chim Acta 2022; 1209:339793. [DOI: 10.1016/j.aca.2022.339793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/28/2022] [Accepted: 03/30/2022] [Indexed: 11/01/2022]
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Hebling E Tavares JP, da Silva Medeiros ML, Barbin DF. Near-infrared techniques for fraud detection in dairy products: A review. J Food Sci 2022; 87:1943-1960. [PMID: 35362099 DOI: 10.1111/1750-3841.16143] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 03/09/2022] [Accepted: 03/16/2022] [Indexed: 01/14/2023]
Abstract
The dairy products sector is an important part of the food industry, and their consumption is expected to grow in the next 10 years. Therefore, the authentication of these products in a faster and precise way is required for the sake of public health. This review proposes the use of near-infrared techniques for the detection of food fraud in dairy products as they are faster, nondestructive, environmentally friendly, do not require sample preparation, and allow multiconstituent analysis. First, we have described frequent forms of food fraud in dairy products and the application of traditional techniques for their detection, highlighting gaps and counterproductive characteristics for the actual global food chain, as longer sample preparation time and use of reagents. Then, the application of near-infrared spectroscopy and hyperspectral imaging for the detection of food fraud mainly in cheese, butter, and yogurt are described. As these techniques depend on model development, the coverage of different dairy products by the literature will promote the identification of food fraud in a faster and reliable way.
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Affiliation(s)
| | | | - Douglas Fernandes Barbin
- Department of Food Engineering, School of Food Engineering, University of Campinas, Campinas, Brazil
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Brasil YL, Cruz-Tirado J, Barbin DF. Fast online estimation of quail eggs freshness using portable NIR spectrometer and machine learning. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108418] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Kaufmann KC, Czakoski A, Barbin DF, da Cunha RL. Incompatibility between sodium caseinate - locust bean gum induced by NaCl and yerba mate extract. Int J Biol Macromol 2021; 183:276-284. [PMID: 33892034 DOI: 10.1016/j.ijbiomac.2021.04.106] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 04/09/2021] [Accepted: 04/17/2021] [Indexed: 11/20/2022]
Abstract
Aqueous two-phase system (ATPS) is a technique used for the separation of biopolymers in two aqueous phases. Some combinations of biopolymers can form a water-in-water (W/W) emulsion due to steric exclusion and thermodynamic incompatibility between these biopolymers under some specific conditions. In this work, the formation of W/W emulsions composed of sodium caseinate (SCN) and locust bean gum (LBG) was evaluated, using NaCl or yerba mate extract as the driving force for the phase separation, which was described by phase's diagrams. Phase diagrams are like fingerprints of ATPS systems, which demonstrate the specific conditions to develop separate phases. Phase diagrams of the two systems show that at the same concentrations of protein and carbohydrate, the addition of NaCl or extract induced the separation of the compounds differently. Salt promotes phase separation by steric exclusion, each phase being rich in one of the polymers. Since extract may also induce other effects, such as the formation of a SCN-extract-LBG complex, migration of LBG to the SCN-rich phase was promoted, modifying the characteristics of the tie lines in the phase diagrams. However, it was feasible to separate the protein in systems containing concentrated phenolic extract, whose incorporation is relevant considering its antioxidant activity.
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Affiliation(s)
- Karine Cristine Kaufmann
- Department of Food Engineering and Technology, School of Food Engineering, University of Campinas, Campinas, SP CEP 13083-862, Brazil
| | - Aline Czakoski
- Department of Food Engineering and Technology, School of Food Engineering, University of Campinas, Campinas, SP CEP 13083-862, Brazil
| | - Douglas Fernandes Barbin
- Department of Food Engineering and Technology, School of Food Engineering, University of Campinas, Campinas, SP CEP 13083-862, Brazil
| | - Rosiane Lopes da Cunha
- Department of Food Engineering and Technology, School of Food Engineering, University of Campinas, Campinas, SP CEP 13083-862, Brazil.
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Scarton M, Nascimento GC, Felisberto MHF, Moro TDMA, Behrens JH, Barbin DF, Clerici MTPS. Muffin with pumpkin flour: technological, sensory and nutritional quality. Braz J Food Technol 2021. [DOI: 10.1590/1981-6723.22920] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Abstract Pumpkin is a yellow or orange-colored vegetable with a mild flavor that stands out as a fiber and nutrient source. However, the products with pumpkins are still little explored in Brazil, due to high perishability in natura and to the lack of eating habits of regional baked goods. This study aimed to produce and characterize a pumpkin flour to be used as an ingredient in muffins, evaluate the technological, and sensory characteristics of these cakes, and select the most sensorially accepted muffin for nutritional composition evaluation. Three muffins were produced as following: one control (M0); and two containing 5 (M1) and 10 (M2) g/100 g of partial substitution of wheat flour by pumpkin flour, thus maintaining the other ingredients common to cakes. The technological characteristics of firmness, volume and color were evaluated. Sensory acceptance was assessed using an affective test on an unstructured 9-point hedonic scale. There were no significant differences in the firmness and image analysis (distribution, size, and pore area of the crumb) among the three muffins, however, M1 and M2 presented darker crust color, more orange crumb, and a lower volume compared to M0. The sensory acceptability of the muffins was considered as a criterion for selection for nutritional composition analysis. The muffins’ acceptance of M1 was better than M0, but without differences to M2. The consumers’ opinions were also considered, and M1 was selected for having greater acceptance. Nutritionally, M0 and M1 presented similar levels of proteins, digestible carbohydrates, and lipids, however, M1 showed a higher content of ash and total dietary fibers: 2.01 ± 0.03 and 1.57 g/100 g (dry basis), respectively. Thus, the pumpkin flour could be produced and used as a regional, enriched, and natural-colored ingredient for muffins or other bakery products, and therefore they had a socially positive impact on family farming.
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Cruz-Tirado J, Fernández Pierna JA, Rogez H, Barbin DF, Baeten V. Authentication of cocoa (Theobroma cacao) bean hybrids by NIR-hyperspectral imaging and chemometrics. Food Control 2020. [DOI: 10.1016/j.foodcont.2020.107445] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Badaró AT, Amigo JM, Blasco J, Aleixos N, Ferreira AR, Clerici MTPS, Barbin DF. Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta. Food Chem 2020; 343:128517. [PMID: 33199118 DOI: 10.1016/j.foodchem.2020.128517] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 10/26/2020] [Accepted: 10/27/2020] [Indexed: 10/23/2022]
Abstract
Pasta is mostly composed by wheat flour and water. Nevertheless, flour can be partially replaced by fibers to provide extra nutrients in the diet. However, fiber can affect the technological quality of pasta if not properly distributed. Usually, determinations of parameters in pasta are destructive and time-consuming. The use of Near Infrared-Hyperspectral Imaging (NIR-HSI), together with machine learning methods, is valuable to improve the efficiency in the assessment of pasta quality. This work aimed to investigate the ability of NIR-HSI and augmented Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) for the evaluation, resolution and quantification of fiber distribution in enriched pasta. Results showed R2V between 0.28 and 0.89, %LOF < 6%, variance explained over 99%, and similarity between pure and recovered spectra over 96% and 98% in models using pure flour and control as initial estimates, respectively, demonstrating the applicability of NIR-HSI and MCR-ALS in the identification of fiber in pasta.
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Affiliation(s)
- Amanda Teixeira Badaró
- Department of Food Engineering, University of Campinas, Rua Monteiro Lobato, 80, Cidade Universitária, 13083-862 Campinas, São Paulo, Brazil; Departamento de Tecnología de Alimentos, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain; Centro de Agroingeniería, Instituto Valenciano de Investigaciones Agrarias (IVIA), Ctra. CV-315, km. 10,7, 46113 Moncada, Valencia, Spain.
| | - José Manuel Amigo
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain; Department of Analytical Chemistry, University of the Basque Country UPV/EHU, P.O. Box 644, 48080 Bilbao, Basque Country, Spain.
| | - Jose Blasco
- Centro de Agroingeniería, Instituto Valenciano de Investigaciones Agrarias (IVIA), Ctra. CV-315, km. 10,7, 46113 Moncada, Valencia, Spain.
| | - Nuria Aleixos
- Departamento de Ingeniería Gráfica, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.
| | - Amanda Rios Ferreira
- Department of Food Technology, University of Campinas, Rua Monteiro Lobato, 80, Cidade Universitária, 13083-862 Campinas, São Paulo, Brazil.
| | - Maria Teresa Pedrosa Silva Clerici
- Department of Food Technology, University of Campinas, Rua Monteiro Lobato, 80, Cidade Universitária, 13083-862 Campinas, São Paulo, Brazil.
| | - Douglas Fernandes Barbin
- Department of Food Engineering, University of Campinas, Rua Monteiro Lobato, 80, Cidade Universitária, 13083-862 Campinas, São Paulo, Brazil.
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de Carvalho LM, Madruga MS, Estévez M, Badaró AT, Barbin DF. Occurrence of wooden breast and white striping in Brazilian slaughtering plants and use of near-infrared spectroscopy and multivariate analysis to identify affected chicken breasts. J Food Sci 2020; 85:3102-3112. [PMID: 32996140 DOI: 10.1111/1750-3841.15465] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 08/20/2020] [Accepted: 09/01/2020] [Indexed: 11/29/2022]
Abstract
White Striping (WS) and Wooden Breast (WB) are emerging poultry myopathies that occur worldwide, affecting the quality of meat. The aim of this study was to evaluate the occurrence of N, WS, WB, and WS/WB (myopathies combined) in chicken breast from Brazilian commercial plant, comparing (1) inspection based on visual aspect and palpation of Pectoralis major muscle, and (2) identification of these myopathies by near-infrared Spectroscopy (NIRS). Chickens slaughtered at Brazilian commercial plant at four age ranges (4 to 5, 6 to 7, 8 to 9, and 65 weeks) were inspected. Spectral information was acquired using a portable NIR spectrometer, and classification models were performed using and Successive Projection Algorithm-Linear Discriminant Analysis (SPA-LDA) and Soft Independent Modeling of Class Analogy (SIMCA) to distinguish normal and affected muscles. Results showed that occurrence of myopathies was aggravated by age of slaughter, as chicken slaughtered at 4 to 5 and 65 weeks exhibited 13.6 and 95% of myopathies, respectively. Birds slaughtered at 65 weeks showed no occurrence of WB, isolated or combined with WS. It was not possible to differentiate the WB and WS/WB classes; therefore, those samples were grouped (WB+WS/WB). SPA-LDA model showed greater accuracy (92 to 93%) in identifying Normal (N), WS, and WB+WS/WB groups, compared to SIMCA (89 to 91%). It can be concluded that the level of occurrence of myopathies in meat is directly related to the age of slaughter. This study demonstrated that NIRS combined with SPA-LDA model could be used as a tool to detect myopathies in chicken breast. This technique has potential for application in industrial processing lines as an alternative to the traditional methods of identification. PRACTICAL APPLICATION: This study shows that NIRS combined with chemometric techniques can be used to identify chicken breast myopathies in a wide range of ages at slaughter. In addition to being able to discriminate chicken muscles into subclasses, namely, Normal, WS, and WB/WB+WS, this technique has potential for application in industrial processing lines as it is a portable and nondestructive method. This procedure is emphasized as an alternative to the conventional method of identification based on palpation and visual assessment of muscle.
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Affiliation(s)
| | - Marta Suely Madruga
- Department of Food Engineering, Federal University of Paraiba, João Pessoa, Paraiba, Brazil
| | - Mario Estévez
- Institute of Meat and Meat Products (IPROCAR), TECAL Research Group, University of Extremadura, Cáceres, Spain
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Badaró AT, Garcia-Martin JF, López-Barrera MDC, Barbin DF, Alvarez-Mateos P. Determination of pectin content in orange peels by near infrared hyperspectral imaging. Food Chem 2020; 323:126861. [PMID: 32334320 DOI: 10.1016/j.foodchem.2020.126861] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 04/03/2020] [Accepted: 04/17/2020] [Indexed: 12/15/2022]
Abstract
Pectin has several purposes in the food and pharmaceutical industry making its quantification important for further extraction. Current techniques for pectin quantification require its extraction using chemicals and producing residues. Determination of pectin content in orange peels was investigated using near infrared hyperspectral imaging (NIR-HSI). Hyperspectral images from orange peel (140 samples) with different amounts of pectin were acquired in the range of 900-2500 nm, and the spectra was used for calibration models using multivariate statistical analyses. Principal component analysis (PCA) and linear discriminant analysis (LDA) showed better results considering three groups: low (0-5%), intermediate (10-40%) and high (50-100%) pectin content. Partial least squares regression (PLSR) models based on full spectra showed higher precision (R2 > 0.93) than those based on few selected wavelengths (R2 between 0.92 and 0.94). The results demonstrate the potential of NIR-HSI to quantify pectin content in orange peels, providing a valuable technique for orange producers and processing industries.
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Affiliation(s)
- Amanda Teixeira Badaró
- Department of Food Engineering, University of Campinas (UNICAMP), Rua Monteiro Lobato, 80, Cidade Universitária, Campinas-SP 13083-862, Brazil.
| | | | | | - Douglas Fernandes Barbin
- Department of Food Engineering, University of Campinas (UNICAMP), Rua Monteiro Lobato, 80, Cidade Universitária, Campinas-SP 13083-862, Brazil.
| | - Paloma Alvarez-Mateos
- Departamento de Ingeniería Química, Facultad de Química, Universidad de Sevilla, Sevilla 41012, Spain.
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Lopes JF, Ludwig L, Barbin DF, Grossmann MVE, Barbon S. Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble. Sensors (Basel) 2019; 19:E2953. [PMID: 31277468 PMCID: PMC6650935 DOI: 10.3390/s19132953] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 04/24/2019] [Accepted: 05/13/2019] [Indexed: 11/16/2022]
Abstract
Imaging sensors are largely employed in the food processing industry for quality control. Flour from malting barley varieties is a valuable ingredient in the food industry, but its use is restricted due to quality aspects such as color variations and the presence of husk fragments. On the other hand, naked varieties present superior quality with better visual appearance and nutritional composition for human consumption. Computer Vision Systems (CVS) can provide an automatic and precise classification of samples, but identification of grain and flour characteristics require more specialized methods. In this paper, we propose CVS combined with the Spatial Pyramid Partition ensemble (SPPe) technique to distinguish between naked and malting types of twenty-two flour varieties using image features and machine learning. SPPe leverages the analysis of patterns from different spatial regions, providing more reliable classification. Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), J48 decision tree, and Random Forest (RF) were compared for samples' classification. Machine learning algorithms embedded in the CVS were induced based on 55 image features. The results ranged from 75.00% (k-NN) to 100.00% (J48) accuracy, showing that sample assessment by CVS with SPPe was highly accurate, representing a potential technique for automatic barley flour classification.
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Affiliation(s)
| | - Leniza Ludwig
- Department of Food Sciences, Londrina State University (UEL), Londrina 86057-970, Brazil
| | | | | | - Sylvio Barbon
- Department of Computer Science, Londrina State University (UEL), Londrina 86057-970, Brazil.
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Kaufmann KC, Favero FDF, de Vasconcelos MAM, Godoy HT, Sampaio KA, Barbin DF. Portable NIR Spectrometer for Prediction of Palm Oil Acidity. J Food Sci 2019; 84:406-411. [PMID: 30758058 DOI: 10.1111/1750-3841.14467] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 11/16/2018] [Accepted: 01/17/2019] [Indexed: 01/23/2023]
Abstract
Palm oil is widely used in the food industry, and its quality is associated with the free fatty acids (FFA) content. Determination of FFA in oil is time-consuming, requires chemicals and generates residues. There is a trend of applying process analytical technologies (PAT) for fast and nondestructive determination of oil parameters. Portable near-infrared (NIR) spectrometers are cheaper than bench top equipment, and have been used for several tasks in the food processing industry, as it provides fast and reliable data for inline measurements. This study investigated the use of NIR spectra using a portable equipment, combined with both unsupervised and supervised multivariate analyses for identification of palm oil samples with different levels of FFA. Soft independent modeling of class analogy , k-Nearest Neighbors, and linear discriminant analysis models were able to correctly identify 100% of the studied samples with selected wavelengths from NIR spectra. Calibration models were performed for acidity prediction, achieving R2 = 0.97, with root mean square error of prediction = 4.37 for partial least squares model using most relevant wavelengths. These results demonstrate the feasibility of applying a low-cost portable NIR spectrophotometer to predict quality parameters of palm oil. PRACTICAL APPLICATION: This work presents results that show the feasibility of using a low-cost portable near-infrared spectrophotometer for the classification of raw palm oil samples according to free fatty acids contents. Regression models are presented as a fast and nondestructive alternative to classify samples for acidity, which is an important quality parameter and that directly affects the market value of crude palm oil.
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Affiliation(s)
- Karine Cristine Kaufmann
- Dept. of Food Engineering, Univ. of Campinas - (UNICAMP), Rua Monteiro Lobato 80 - Cidade Universitária Zeferino Vaz, CEP 13083-862, Campinas, SP, Brazil
| | - Flávia de Faveri Favero
- Dept. of Food Engineering, Univ. of Campinas - (UNICAMP), Rua Monteiro Lobato 80 - Cidade Universitária Zeferino Vaz, CEP 13083-862, Campinas, SP, Brazil
| | - Marcus Arthur Marçal de Vasconcelos
- Dept. of Food Science, Univ. of Campinas - (UNICAMP), Rua Monteiro Lobato 80 - Cidade Universitária Zeferino Vaz, CEP 13083-862, Campinas, SP, Brazil
| | - Helena Teixeira Godoy
- Dept. of Food Science, Univ. of Campinas - (UNICAMP), Rua Monteiro Lobato 80 - Cidade Universitária Zeferino Vaz, CEP 13083-862, Campinas, SP, Brazil
| | - Klicia Araujo Sampaio
- Dept. of Food Engineering, Univ. of Campinas - (UNICAMP), Rua Monteiro Lobato 80 - Cidade Universitária Zeferino Vaz, CEP 13083-862, Campinas, SP, Brazil
| | - Douglas Fernandes Barbin
- Dept. of Food Engineering, Univ. of Campinas - (UNICAMP), Rua Monteiro Lobato 80 - Cidade Universitária Zeferino Vaz, CEP 13083-862, Campinas, SP, Brazil
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Nolasco Perez IM, Badaró AT, Barbon S, Barbon APA, Pollonio MAR, Barbin DF. Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning. Appl Spectrosc 2018; 72:1774-1780. [PMID: 30063378 DOI: 10.1177/0003702818788878] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Identification of different chicken parts using portable equipment could provide useful information for the processing industry and also for authentication purposes. Traditionally, physical-chemical analysis could deal with this task, but some disadvantages arise such as time constraints and requirements of chemicals. Recently, near-infrared (NIR) spectroscopy and machine learning (ML) techniques have been widely used to obtain a rapid, noninvasive, and precise characterization of biological samples. This study aims at classifying chicken parts (breasts, thighs, and drumstick) using portable NIR equipment combined with ML algorithms. Physical and chemical attributes (pH and L*a*b* color features) and chemical composition (protein, fat, moisture, and ash) were determined for each sample. Spectral information was acquired using a portable NIR spectrophotometer within the range 900-1700 nm and principal component analysis was used as screening approach. Support vector machine and random forest algorithms were compared for chicken meat classification. Results confirmed the possibility of differentiating breast samples from thighs and drumstick with 98.8% accuracy. The results showed the potential of using a NIR portable spectrophotometer combined with a ML approach for differentiation of chicken parts in the processing industry.
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Affiliation(s)
| | | | - Sylvio Barbon
- Department of Computer Science, Londrina State University (UEL), Brazil
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Barbin DF, Maciel LF, Bazoni CHV, Ribeiro MDS, Carvalho RDS, Bispo EDS, Miranda MDPS, Hirooka EY. Classification and compositional characterization of different varieties of cocoa beans by near infrared spectroscopy and multivariate statistical analyses. J Food Sci Technol 2018; 55:2457-2466. [PMID: 30042561 DOI: 10.1007/s13197-018-3163-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 03/28/2018] [Accepted: 04/09/2018] [Indexed: 11/28/2022]
Abstract
Effective and fast methods are important for distinguishing cocoa varieties in the field and in the processing industry. This work proposes the application of NIR spectroscopy as a potential analytical method to classify different varieties and predict the chemical composition of cocoa. Chemical composition and colour features were determined by traditional methods and then related with the spectral information by partial least-squares regression. Several mathematical pre-processing methods including first and second derivatives, standard normal variate and multiplicative scatter correction were applied to study the influence of spectral variations. The results of chemical composition analysis and colourimetric measurements show significant differences between varieties. NIR spectra of samples exhibited characteristic profiles for each variety and principal component analysis showed different varieties in according to spectral features.
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Affiliation(s)
- Douglas Fernandes Barbin
- 1Department of Food Engineering, University of Campinas, Rua Monteiro Lobato, 80. Cidade Universitária, Campinas, SP CEP 13083-860 Brazil
| | - Leonardo Fonseca Maciel
- 2College of Pharmacy, Federal University of Bahia, Salvador, Bahia Brazil.,3Department of Food Science and Technology, State University of Londrina, Rodovia Celso Garcia Cid, PR 445 Km 380, Campus Universitário, Londrina, PR 86055-900 Brazil
| | - Carlos Henrique Vidigal Bazoni
- 3Department of Food Science and Technology, State University of Londrina, Rodovia Celso Garcia Cid, PR 445 Km 380, Campus Universitário, Londrina, PR 86055-900 Brazil
| | | | | | | | | | - Elisa Yoko Hirooka
- 3Department of Food Science and Technology, State University of Londrina, Rodovia Celso Garcia Cid, PR 445 Km 380, Campus Universitário, Londrina, PR 86055-900 Brazil
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Moro TMA, Celegatti CM, Pereira APA, Lopes AS, Barbin DF, Pastore GM, Clerici MTPS. Use of burdock root flour as a prebiotic ingredient in cookies. Lebensm Wiss Technol 2018. [DOI: 10.1016/j.lwt.2017.12.059] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Watanabe LS, Bovolenta YR, Acquaro Junior VR, Barbin DF, Madeira TB, Nixdorf SL. <b>Investigation of NIR spectra pre-processing methods combined with multivariate regression for determination of moisture in powdered industrial egg. Acta Sci Technol 2018. [DOI: 10.4025/actascitechnol.v40i1.30133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Barbin DF, Kaminishikawahara CM, Soares AL, Mizubuti IY, Grespan M, Shimokomaki M, Hirooka EY. Prediction of chicken quality attributes by near infrared spectroscopy. Food Chem 2015; 168:554-60. [DOI: 10.1016/j.foodchem.2014.07.101] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2013] [Revised: 06/14/2014] [Accepted: 07/21/2014] [Indexed: 11/17/2022]
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Barbin DF, Felicio ALDSM, Sun DW, Nixdorf SL, Hirooka EY. Application of infrared spectral techniques on quality and compositional attributes of coffee: An overview. Food Res Int 2014. [DOI: 10.1016/j.foodres.2014.01.005] [Citation(s) in RCA: 136] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Barbin DF, Neves Filho LC, Silveira Junior V. Processo de congelamento em túnel portátil com convecção forçada por exaustão e insuflação para paletes. Ciênc Tecnol Aliment 2009. [DOI: 10.1590/s0101-20612009000300033] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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