1
|
Alves V, Dos Santos JM, Viegas O, Pinto E, Ferreira IMPLVO, Aparecido Lima V, Felsner ML. An eco-friendly approach for analysing sugars, minerals, and colour in brown sugar using digital image processing and machine learning. Food Res Int 2024; 191:114673. [PMID: 39059905 DOI: 10.1016/j.foodres.2024.114673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 06/09/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024]
Abstract
Brown sugar is a natural sweetener obtained by thermal processing, with interesting nutritional characteristics. However, it has significant sensory variability, which directly affects product quality and consumer choice. Therefore, developing rapid methods for its quality control is desirable. This work proposes a fast, environmentally friendly, and accurate method for the simultaneous analysis of sucrose, reducing sugars, minerals and ICUMSA colour in brown sugar, using an innovative strategy that combines digital image processing acquired by smartphone cell with machine learning. Data extracted from the digital images, as well as experimentally determined contents of the physicochemical characteristics and elemental profile were the variables adopted for building predictive regression models by applying the kNN algorithm. The models achieved the highest predictive capacity for the Ca, ICUMSA colour, Fe and Zn, with coefficients of determination (R2) ≥ 92.33 %. Lower R2 values were observed for sucrose (81.16 %), reducing sugars (85.67 %), Mn (83.36 %) and Mg (86.97 %). Low data dispersion was found for all the predictive models generated (RMSE < 0.235). The AGREE Metric assessed the green profile and determined that the proposed approach is superior in relation to conventional methods because it avoids the use of solvents and toxic reagents, consumes minimal energy, produces no toxic waste, and is safer for analysts. The combination of digital image processing (DIP) and the kNN algorithm provides a fast, non-invasive and sustainable analytical approach. It streamlines and improves quality control of brown sugar, enabling the production of sweeteners that meet consumer demands and industry standards.
Collapse
Affiliation(s)
- Vandressa Alves
- Department of Chemistry, State University of Midwestern at Paraná (UNICENTRO), Vila Carli, Zip Code 85040-080, Guarapuava City, Paraná, Brazil.
| | - Jeferson M Dos Santos
- Department of Chemistry, State University of Midwestern at Paraná (UNICENTRO), Vila Carli, Zip Code 85040-080, Guarapuava City, Paraná, Brazil.
| | - Olga Viegas
- LAQV/REQUIMTE, Faculty of Nutrition and Food Science of the University of Porto, Zip Code 4150-180, Porto, Portugal.
| | - Edgar Pinto
- REQUIMTE/LAQV, ESS, Polytechnic of Porto, Zip Code 4200-072, Porto, Portugal
| | - Isabel M P L V O Ferreira
- LAQV/REQUIMTE, Chemical Sciences Department, Faculty of Pharmacy, University of Porto, Zip Code 4050-313 Porto, Portugal.
| | - Vanderlei Aparecido Lima
- Department of Chemistry, Federal University of Technology - Paraná (UTFPR), Zip Code 85503-390, Pato Branco City, Paraná, Brazil.
| | - Maria L Felsner
- Department of Chemistry, State University of Midwestern at Paraná (UNICENTRO), Vila Carli, Zip Code 85040-080, Guarapuava City, Paraná, Brazil; Department of Chemistry, State University of Londrina (UEL), Zip Code 86057-970, Londrina City, Paraná, Brazil.
| |
Collapse
|
2
|
Giussani B, Gorla G, Riu J. Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview. Crit Rev Anal Chem 2024; 54:11-43. [PMID: 35286178 DOI: 10.1080/10408347.2022.2047607] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.
Collapse
Affiliation(s)
- Barbara Giussani
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Giulia Gorla
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Jordi Riu
- Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Tarragona, Spain
| |
Collapse
|
3
|
Ong P, Jian J, Li X, Zou C, Yin J, Ma G. New approach for sugarcane disease recognition through visible and near-infrared spectroscopy and a modified wavelength selection method using machine learning models. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 302:123037. [PMID: 37356390 DOI: 10.1016/j.saa.2023.123037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/08/2023] [Accepted: 06/16/2023] [Indexed: 06/27/2023]
Abstract
The proliferation of pathogenic fungi in sugarcane crops poses a significant threat to agricultural productivity and economic sustainability. Early identification and management of sugarcane diseases are therefore crucial to mitigate the adverse impacts of these pathogens. In this study, visible and near-infrared spectroscopy (380-1400 nm) combined with a novel wavelength selection method, referred to as modified flower pollination algorithm (MFPA), was utilized for sugarcane disease recognition. The selected wavelengths were incorporated into machine learning models, including Naïve Bayes, random forest, and support vector machine (SVM). The developed simplified SVM model, which utilized the MFPA wavelength selection method yielded the best performances, achieving a precision value of 0.9753, a sensitivity value of 0.9259, a specificity value of 0.9524, and an accuracy of 0.9487. These results outperformed those obtained by other wavelength selection approaches, including the selectivity ratio, variable importance in projection, and the baseline method of the flower pollination algorithm.
Collapse
Affiliation(s)
- Pauline Ong
- College of Mathematics and Physics, Center for Applied Mathematics of Guangxi, Guangxi Minzu University, Nanning 530006, China; Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia.
| | - Jinbao Jian
- College of Mathematics and Physics, Center for Applied Mathematics of Guangxi, Guangxi Minzu University, Nanning 530006, China; Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi Minzu University, Nanning 530006, China.
| | - Xiuhua Li
- School of Electrical Engineering, Guangxi University, Nanning 530005, China; Guangxi Key Laboratory of Sugarcane Biology, Guangxi University, Nanning 530005, China.
| | - Chengwu Zou
- Guangxi Key Laboratory of Sugarcane Biology and College of Agriculture, Guangxi University, Nanning 530005, China; State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning 530005, China.
| | - Jianghua Yin
- College of Mathematics and Physics, Center for Applied Mathematics of Guangxi, Guangxi Minzu University, Nanning 530006, China.
| | - Guodong Ma
- College of Mathematics and Physics, Center for Applied Mathematics of Guangxi, Guangxi Minzu University, Nanning 530006, China.
| |
Collapse
|
4
|
Gullifa G, Barone L, Papa E, Giuffrida A, Materazzi S, Risoluti R. Portable NIR spectroscopy: the route to green analytical chemistry. Front Chem 2023; 11:1214825. [PMID: 37818482 PMCID: PMC10561305 DOI: 10.3389/fchem.2023.1214825] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/07/2023] [Indexed: 10/12/2023] Open
Abstract
There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.
Collapse
Affiliation(s)
- G. Gullifa
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - L. Barone
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - E. Papa
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - A. Giuffrida
- Department of Chemical Sciences, University of Catania, Catania, Italy
| | - S. Materazzi
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - R. Risoluti
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| |
Collapse
|
5
|
Genangeli A, Allasia G, Bindi M, Cantini C, Cavaliere A, Genesio L, Giannotta G, Miglietta F, Gioli B. A Novel Hyperspectral Method to Detect Moldy Core in Apple Fruits. SENSORS 2022; 22:s22124479. [PMID: 35746261 PMCID: PMC9230990 DOI: 10.3390/s22124479] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/28/2022] [Accepted: 06/10/2022] [Indexed: 02/04/2023]
Abstract
An innovative low-cost device based on hyperspectral spectroscopy in the near infrared (NIR) spectral region is proposed for the non-invasive detection of moldy core (MC) in apples. The system, based on light collection by an integrating sphere, was tested on 70 apples cultivar (cv) Golden Delicious infected by Alternaria alternata, one of the main pathogens responsible for MC disease. Apples were sampled in vertical and horizontal positions during five measurement rounds in 13 days’ time, and 700 spectral signatures were collected. Spectral correlation together with transmittance temporal patterns and ANOVA showed that the spectral region from 863.38 to 877.69 nm was most linked to MC presence. Then, two binary classification models based on Artificial Neural Network Pattern Recognition (ANN-AP) and Bagging Classifier (BC) with decision trees were developed, revealing a better detection capability by ANN-AP, especially in the early stage of infection, where the predictive accuracy was 100% at round 1 and 97.15% at round 2. In subsequent rounds, the classification results were similar in ANN-AP and BC models. The system proposed surpassed previous MC detection methods, needing only one measurement per fruit, while further research is needed to extend it to different cultivars or fruits.
Collapse
Affiliation(s)
- Andrea Genangeli
- Department of Agronomy and Land Management, University of Florence, P.le delle Cascine 18, 50144 Florence, Italy; (A.G.); (M.B.)
| | - Giorgio Allasia
- Gruppo FOS, Via Enrico Melen, 77/ed.A, 16152 Genova, Italy; (G.A.); (G.G.)
| | - Marco Bindi
- Department of Agronomy and Land Management, University of Florence, P.le delle Cascine 18, 50144 Florence, Italy; (A.G.); (M.B.)
| | - Claudio Cantini
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy; (C.C.); (A.C.); (L.G.); (F.M.)
| | - Alice Cavaliere
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy; (C.C.); (A.C.); (L.G.); (F.M.)
| | - Lorenzo Genesio
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy; (C.C.); (A.C.); (L.G.); (F.M.)
| | - Giovanni Giannotta
- Gruppo FOS, Via Enrico Melen, 77/ed.A, 16152 Genova, Italy; (G.A.); (G.G.)
| | - Franco Miglietta
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy; (C.C.); (A.C.); (L.G.); (F.M.)
| | - Beniamino Gioli
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy; (C.C.); (A.C.); (L.G.); (F.M.)
- Correspondence:
| |
Collapse
|
6
|
Aznan A, Gonzalez Viejo C, Pang A, Fuentes S. Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies. Foods 2022; 11:1181. [PMID: 35563907 PMCID: PMC9105373 DOI: 10.3390/foods11091181] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/18/2022] [Accepted: 04/18/2022] [Indexed: 12/10/2022] Open
Abstract
Aroma and other physicochemical parameters are important attributes influencing consumer perception and acceptance of rice. However, current methods using multiple instruments and laboratory analysis make these assessments costly and time-consuming. Therefore, this study aimed to assess rice quality traits of 17 commercial rice types using a low-cost electronic nose and portable near-infrared spectrometer coupled with machine learning (ML). Specifically, artificial neural networks (ANN) were used to classify the type of rice and predict rice quality traits (aromas, color, texture, and pH of cooked rice) as targets. The ML models developed showed that the chemometrics obtained from both sensor technologies successfully classified the rice (Model 1: 98.7%; Model 2: 98.6%) and predicted the peak area of aromas obtained by gas chromatography-mass spectroscopy found in raw (Model 3: R = 0.95; Model 6: R = 0.95) and cooked rice (Model 4: R = 0.98; Model 7: R = 0.96). Furthermore, a high R = 0.98 was obtained for Model 5 to estimate the color, texture, and pH of cooked rice. The proposed method is rapid, low-cost, reliable, and may help the rice industry increase high-quality rice production and accelerate the adoption of digital technologies and artificial intelligence to support the rice value chain.
Collapse
Affiliation(s)
- Aimi Aznan
- Digital Agriculture, Food and Wine Group (DAFW), School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (A.A.); (C.G.V.); (A.P.)
- Faculty of Chemical Engineering Technology, University Malaysia Perlis, Arau 02600, Perlis, Malaysia
| | - Claudia Gonzalez Viejo
- Digital Agriculture, Food and Wine Group (DAFW), School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (A.A.); (C.G.V.); (A.P.)
| | - Alexis Pang
- Digital Agriculture, Food and Wine Group (DAFW), School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (A.A.); (C.G.V.); (A.P.)
| | - Sigfredo Fuentes
- Digital Agriculture, Food and Wine Group (DAFW), School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (A.A.); (C.G.V.); (A.P.)
| |
Collapse
|