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Lee SD, Gil CS, Lee JH, Jeong HB, Kim JH, Jang YA, Kim DY, Lee WM, Moon JH. Internal quality prediction technology for 'Sulhyang' strawberry fruit using organic analysis and hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 323:124912. [PMID: 39142263 DOI: 10.1016/j.saa.2024.124912] [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: 04/03/2024] [Revised: 07/05/2024] [Accepted: 07/29/2024] [Indexed: 08/16/2024]
Abstract
In recent years, hyperspectral imaging combined with machine learning techniques has garnered significant attention for its potential in assessing fruit maturity. This study proposes a method for predicting strawberry fruit maturity based on the harvest time. The main features of this study are as follows. 1) Selection of wavelength band associated with strawberry growth season; 2) Extraction of efficient parameters to predict strawberry maturity 3) Prediction of internal quality attributes of strawberries using extracted parameters. In this study, experts cultivated strawberries in a controlled environment and performed hyperspectral measurements and organic analyses on the fruit with minimal time delay to facilitate accurate modeling. Data augmentation techniques through cross-validation and interpolation were effective in improving model performance. The four parameters included in the model and the cumulative value of the model were available for quality prediction as additional parameters. Among these five parameter candidates, two parameters with linearity were finally identified. The predictive outcomes for firmness, soluble solids content, acidity, and anthocyanin levels in strawberry fruit, based on the two identified parameters, are as follows: The first parameter, ps, demonstrated RMSE performances of 1.0 N, 2.3 %, 0.1 %, and 2.0 mg per 100 g fresh fruit for firmness, soluble solids content, acidity, and anthocyanin, respectively. The second parameter, p3, showed RMSE performances of 0.6 N, 1.2 %, 0.1 %, and 1.8 mg per 100 g fresh fruit, respectively. The proposed non-destructive analysis method shows the potential to overcome the challenges associated with destructive testing methods for assessing certain internal qualities of strawberry fruit.
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Affiliation(s)
- Sang-Deok Lee
- Vegetable Research Division, National Institute of Horticultural and Herbal Science (NIHHS), Rural Development Administration (RDA), Wanju-gun 55365, Republic of Korea.
| | - Chan-Saem Gil
- Vegetable Research Division, National Institute of Horticultural and Herbal Science (NIHHS), Rural Development Administration (RDA), Wanju-gun 55365, Republic of Korea; Department of Horticulture, College of Industrial Science, Kongju National University, Yesan 32439, Republic of Korea
| | - Jun-Ho Lee
- Vegetable Research Division, National Institute of Horticultural and Herbal Science (NIHHS), Rural Development Administration (RDA), Wanju-gun 55365, Republic of Korea
| | - Hyo-Bong Jeong
- Vegetable Research Division, National Institute of Horticultural and Herbal Science (NIHHS), Rural Development Administration (RDA), Wanju-gun 55365, Republic of Korea
| | - Jin-Hee Kim
- Vegetable Research Division, National Institute of Horticultural and Herbal Science (NIHHS), Rural Development Administration (RDA), Wanju-gun 55365, Republic of Korea
| | - Yun-Ah Jang
- Vegetable Research Division, National Institute of Horticultural and Herbal Science (NIHHS), Rural Development Administration (RDA), Wanju-gun 55365, Republic of Korea
| | - Dae-Young Kim
- Vegetable Research Division, National Institute of Horticultural and Herbal Science (NIHHS), Rural Development Administration (RDA), Wanju-gun 55365, Republic of Korea
| | - Woo-Moon Lee
- Vegetable Research Division, National Institute of Horticultural and Herbal Science (NIHHS), Rural Development Administration (RDA), Wanju-gun 55365, Republic of Korea
| | - Ji-Hye Moon
- Vegetable Research Division, National Institute of Horticultural and Herbal Science (NIHHS), Rural Development Administration (RDA), Wanju-gun 55365, Republic of Korea
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Rapid evaluation of texture parameters of Tan mutton using hyperspectral imaging with optimization algorithms. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108815] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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3
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Hernández-Ramos P, Vivar-Quintana AM, Revilla I, González-Martín MI, Hernández-Jiménez M, Martínez-Martín I. Prediction of Sensory Parameters of Cured Ham: A Study of the Viability of the Use of NIR Spectroscopy and Artificial Neural Networks. SENSORS 2020; 20:s20195624. [PMID: 33019622 PMCID: PMC7584045 DOI: 10.3390/s20195624] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 09/20/2020] [Accepted: 09/28/2020] [Indexed: 11/16/2022]
Abstract
Dry-cured ham is a high-quality product owing to its organoleptic characteristics. Sensory analysis is an essential part of assessing its quality. However, sensory assessment is a laborious process which implies the availability of a trained tasting panel. The aim of this study was the prediction of dry-ham sensory characteristics by means of an instrumental technique. To do so, an artificial neural network (ANN) model for the prediction of sensory parameters of dry-cured hams based on NIR spectral information was developed and optimized. The NIR spectra were obtained with a fiber-optic probe applied directly to the ham sample. In order to achieve this objective, the neural network was designed using 28 sensory parameters analyzed by a trained panel for sensory profile analysis as output data. A total of 91 samples of dry-cured ham matured for 24 months were analyzed. The hams corresponded to two different breeds (Iberian and Iberian x Duroc) and two different feeding systems (feeding outdoors with acorns or feeding with concentrates). The training algorithm and ANN architecture (the number of neurons in the hidden layer) used for the training were optimized. The parameters of ANN architecture analyzed have been shown to have an effect on the prediction capacity of the network. The Levenberg–Marquardt training algorithm has been shown to be the most suitable for the application of an ANN to sensory parameters
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Affiliation(s)
- Pedro Hernández-Ramos
- Graphic Expression in Engineering, University of Salamanca, Escuela Politécnica Superior de Zamora, Avenida Requejo 33, 49022 Zamora, Spain;
| | - Ana María Vivar-Quintana
- Food Technology, University of Salamanca, Escuela Politécnica Superior de Zamora, Avenida Requejo 33, 49022 Zamora, Spain; (I.R.); (M.H.-J.); (I.M.-M.)
- Correspondence:
| | - Isabel Revilla
- Food Technology, University of Salamanca, Escuela Politécnica Superior de Zamora, Avenida Requejo 33, 49022 Zamora, Spain; (I.R.); (M.H.-J.); (I.M.-M.)
| | - María Inmaculada González-Martín
- Analytical Chemistry, Nutrition and Bromatology, University of Salamanca, Calle Plaza de los Caidos s/n, 37008 Salamanca, Spain;
| | - Miriam Hernández-Jiménez
- Food Technology, University of Salamanca, Escuela Politécnica Superior de Zamora, Avenida Requejo 33, 49022 Zamora, Spain; (I.R.); (M.H.-J.); (I.M.-M.)
| | - Iván Martínez-Martín
- Food Technology, University of Salamanca, Escuela Politécnica Superior de Zamora, Avenida Requejo 33, 49022 Zamora, Spain; (I.R.); (M.H.-J.); (I.M.-M.)
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Choi JY, Moon KD. Non-destructive discrimination of sesame oils via hyperspectral image analysis. J Food Compost Anal 2020. [DOI: 10.1016/j.jfca.2020.103505] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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5
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Gonell F, Botas AMP, Brites CDS, Amorós P, Carlos LD, Julián-López B, Ferreira RAS. Aggregation-induced heterogeneities in the emission of upconverting nanoparticles at the submicron scale unfolded by hyperspectral microscopy. NANOSCALE ADVANCES 2019; 1:2537-2545. [PMID: 36132713 PMCID: PMC9418934 DOI: 10.1039/c8na00412a] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 04/28/2019] [Indexed: 05/23/2023]
Abstract
Transparent upconverting hybrid nanocomposites are exciting materials for advanced applications such as 3D displays, nanosensors, solar energy converters, and fluorescence biomarkers. This work presents a simple strategy to disperse upconverting β-NaYF4:Yb3+/Er3+ or Tm3+ nanoparticles into low cost, widely used and easy-to-process polydimethylsiloxane (PDMS)-based organic-inorganic hybrids. The upconverting hybrids were shaped as monoliths, films or powders displaying in the whole volume Tm3+ or Er3+ emissions (in the violet/blue and green/red spectral regions, respectively). For the first time, hyperspectral microscopy allows the identification at the submicron scale of differences in the hybrids' emission colour, due to variations in the relative intensity of the distinct components of the upconversion spectrum. The effect is attributed to the size distribution of the agglomerates of nanoparticles, highlighting the importance of studying the emission at submicron scales, since this effect is not observable in measurements recorded in larger areas.
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Affiliation(s)
- Francisco Gonell
- Institute of Advanced Materials (INAM), Universitat Jaume I Castellón de la Plana 12006 Spain
| | - Alexandre M P Botas
- Departamento de Física and CICECO - Aveiro Institute of Materials, Universidade de Aveiro 3810-193 Aveiro Portugal
| | - Carlos D S Brites
- Departamento de Física and CICECO - Aveiro Institute of Materials, Universidade de Aveiro 3810-193 Aveiro Portugal
| | - Pedro Amorós
- Institute of Materials Science (ICMUV), University of Valencia Catedrático José Beltrán 2 46980 Paterna Valencia Spain
| | - Luís D Carlos
- Departamento de Física and CICECO - Aveiro Institute of Materials, Universidade de Aveiro 3810-193 Aveiro Portugal
| | - Beatriz Julián-López
- Institute of Advanced Materials (INAM), Universitat Jaume I Castellón de la Plana 12006 Spain
| | - Rute A S Ferreira
- Departamento de Física and CICECO - Aveiro Institute of Materials, Universidade de Aveiro 3810-193 Aveiro Portugal
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Potential of hyperspectral imaging for rapid identification of true and false honeysuckle tea leaves. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2018. [DOI: 10.1007/s11694-018-9834-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Spectral Detection Techniques for Non-Destructively Monitoring the Quality, Safety, and Classification of Fresh Red Meat. FOOD ANAL METHOD 2018. [DOI: 10.1007/s12161-018-1256-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Munera S, Amigo JM, Aleixos N, Talens P, Cubero S, Blasco J. Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine. Food Control 2018. [DOI: 10.1016/j.foodcont.2017.10.037] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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9
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Feng CH, Makino Y, Oshita S, García Martín JF. Hyperspectral imaging and multispectral imaging as the novel techniques for detecting defects in raw and processed meat products: Current state-of-the-art research advances. Food Control 2018. [DOI: 10.1016/j.foodcont.2017.07.013] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Arendse E, Fawole OA, Magwaza LS, Opara UL. Non-destructive prediction of internal and external quality attributes of fruit with thick rind: A review. J FOOD ENG 2018. [DOI: 10.1016/j.jfoodeng.2017.08.009] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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Wang J, Zhang M, Gao Z, Adhikari B. Smart storage technologies applied to fresh foods: A review. Crit Rev Food Sci Nutr 2017; 58:2689-2699. [PMID: 28665695 DOI: 10.1080/10408398.2017.1323722] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Fresh foods are perishable, seasonal and regional in nature and their storage, transportation, and preservation of freshness are quite challenging. Smart storage technologies can online detection and monitor the changes of quality parameters and storage environment of fresh foods during storage, so that operators can make timely adjustments to reduce the loss. This article reviews the smart storage technologies from two aspects: online detection technologies and smartly monitoring technologies for fresh foods. Online detection technologies include electronic nose, nuclear magnetic resonance (NMR), near infrared spectroscopy (NIRS), hyperspectral imaging and computer vision. Smartly monitoring technologies mainly include some intelligent indicators for monitoring the change of storage environment. Smart storage technologies applied to fresh foods need to be highly efficient and nondestructive and need to be competitively priced. In this work, we have critically reviewed the principles, applications, and development trends of smart storage technologies.
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Affiliation(s)
- Jingyu Wang
- a State Key Laboratory of Food Science and Technology , Jiangnan University , Wuxi , Jiangsu , China
| | - Min Zhang
- a State Key Laboratory of Food Science and Technology , Jiangnan University , Wuxi , Jiangsu , China.,b Jiangnan University(Yangzhou) Food Biotechnology Institute , Yangzhou , China
| | - Zhongxue Gao
- c Wuxi Delin Boat Equipment Co. , Wuxi , Jiangsu , China
| | - Benu Adhikari
- d School of Science, RMIT University , Melbourne , VIC3083 , Australia
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Evaluation of spectral imaging for inspection of adulterants in terms of common wheat flour, cassava flour and corn flour in organic Avatar wheat ( Triticum spp.) flour. J FOOD ENG 2017. [DOI: 10.1016/j.jfoodeng.2016.12.014] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Barbieri S, Soglia F, Palagano R, Tesini F, Bendini A, Petracci M, Cavani C, Gallina Toschi T. Sensory and rapid instrumental methods as a combined tool for quality control of cooked ham. Heliyon 2016; 2:e00202. [PMID: 27957555 PMCID: PMC5133677 DOI: 10.1016/j.heliyon.2016.e00202] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Revised: 09/26/2016] [Accepted: 11/18/2016] [Indexed: 10/30/2022] Open
Abstract
In this preliminary investigation, different commercial categories of Italian cooked pork hams have been characterized using an integrated approach based on both sensory and fast instrumental measurements. For these purposes, Italian products belonging to different categories (cooked ham, "selected" cooked ham and "high quality" cooked ham) were evaluated by sensory descriptive analysis and by the application of rapid tools such as image analysis by an "electronic eye" and texture analyzer. The panel of trained assessors identified and evaluated 10 sensory descriptors able to define the quality of the products. Statistical analysis highlighted that sensory characteristics related to appearance and texture were the most significant in discriminating samples belonged to the highest (high quality cooked hams) and the lowest (cooked hams) quality of the product whereas the selected cooked hams, showed intermediate characteristics. In particular, high quality samples were characterized, above all, by the highest intensity of pink intensity, typical appearance and cohesiveness, and, at the same time, by the lowest intensity of juiciness; standard cooked ham samples showed the lowest intensity of all visual attributes and the highest value of juiciness, whereas the intermediate category (selected cooked ham) was not discriminated from the other. Also physical-rheological parameters measured by electronic eye and texture analyzer were effective in classifying samples. In particular, the PLS model built with data obtained from the electronic eye showed a satisfactory performance in terms of prediction of the pink intensity and presence of fat attributes evaluated during the sensory visual phase. This study can be considered a first application of this combined approach that could represent a suitable and fast method to verify if the meat product purchased by consumer match its description in terms of compliance with the claimed quality.
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Affiliation(s)
- Sara Barbieri
- Department of Agricultural and Food Sciences, Alma Mater Studiorum, University of Bologna, Cesena (FC), Italy
| | - Francesca Soglia
- Department of Agricultural and Food Sciences, Alma Mater Studiorum, University of Bologna, Cesena (FC), Italy
| | - Rosa Palagano
- Department of Agricultural and Food Sciences, Alma Mater Studiorum, University of Bologna, Cesena (FC), Italy
| | - Federica Tesini
- Interdepartmental Centre for Industrial Agrofood Research, Alma Mater Studiorum, University of Bologna, Cesena (FC), Italy
| | - Alessandra Bendini
- Department of Agricultural and Food Sciences, Alma Mater Studiorum, University of Bologna, Cesena (FC), Italy; Interdepartmental Centre for Industrial Agrofood Research, Alma Mater Studiorum, University of Bologna, Cesena (FC), Italy
| | - Massimiliano Petracci
- Department of Agricultural and Food Sciences, Alma Mater Studiorum, University of Bologna, Cesena (FC), Italy; Interdepartmental Centre for Industrial Agrofood Research, Alma Mater Studiorum, University of Bologna, Cesena (FC), Italy
| | - Claudio Cavani
- Department of Agricultural and Food Sciences, Alma Mater Studiorum, University of Bologna, Cesena (FC), Italy; Interdepartmental Centre for Industrial Agrofood Research, Alma Mater Studiorum, University of Bologna, Cesena (FC), Italy
| | - Tullia Gallina Toschi
- Department of Agricultural and Food Sciences, Alma Mater Studiorum, University of Bologna, Cesena (FC), Italy; Interdepartmental Centre for Industrial Agrofood Research, Alma Mater Studiorum, University of Bologna, Cesena (FC), Italy
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Sun Y, Liu Y, Yu H, Xie A, Li X, Yin Y, Duan X. Non-Destructive Prediction of Moisture Content and Freezable Water Content of Purple-Fleshed Sweet Potato Slices during Drying Process Using Hyperspectral Imaging Technique. FOOD ANAL METHOD 2016. [DOI: 10.1007/s12161-016-0722-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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15
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Pan L, Zhang Q, Zhang W, Sun Y, Hu P, Tu K. Detection of cold injury in peaches by hyperspectral reflectance imaging and artificial neural network. Food Chem 2015; 192:134-41. [PMID: 26304330 DOI: 10.1016/j.foodchem.2015.06.106] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2015] [Revised: 06/26/2015] [Accepted: 06/30/2015] [Indexed: 11/28/2022]
Abstract
Peaches in cold storage may develop chill damage, as symptomized by deteriorated texture and lack of juice. To examine fruit quality, we established a hyperspectral imaging system to detect cold injury, and an artificial neural network (ANN) model was developed for which eight optimal wavelengths were selected. Between normal and chill-damaged peaches, significant differences in fruit quality parameters and the spectral response to correlating selected wavelengths were observed. Evidencing this relationship, the correlation coefficients between quality parameters and the respective spectral response of eight selected wavelengths were -0.587 to -0.700, 0.393 to 0.552, 0.510 to 0.751, and 0.574 to 0.773. With optimal representative wavelengths as inputs for the ANN model, the overall classification accuracy of chill damage was 95.8% for all cold-stored samples. The ANN prediction models for quality parameters performed well, with correlation coefficients from 0.6979 to 0.9026. This research demonstrates feasibility of hyperspectral reflectance imaging technique for detecting cold injury.
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Affiliation(s)
- Leiqing Pan
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, PR China.
| | - Qiang Zhang
- Chongming Food and Drug Administration, Shanghai, No. 128, Renmin Road, Chengqiao Town, Chongming County 202150, PR China
| | - Wei Zhang
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, PR China
| | - Ye Sun
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, PR China
| | - Pengcheng Hu
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, PR China
| | - Kang Tu
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, PR China
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Abou Fadel M, Zhang X, de Juan A, Tauler R, Vezin H, Duponchel L. Extraction of Pure Spectral Signatures and Corresponding Chemical Maps from EPR Imaging Data Sets: Identifying Defects on a CaF2 Surface Due to a Laser Beam Exposure. Anal Chem 2015; 87:3929-35. [DOI: 10.1021/ac504733u] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Affiliation(s)
- Maya Abou Fadel
- LASIR
CNRS UMR 8516, Université Lille1, Sciences et Technologies, 59655 Villeneuve d’Ascq Cedex, France
| | - Xin Zhang
- IDAEA-CSIC, Jordi Girona 18, 08028 Barcelona, Spain
| | - Anna de Juan
- Chemometrics
Group, Department of Analytical Chemistry, Universitat de Barcelona, Diagonal 645, 08028 Barcelona, Spain
| | - Roma Tauler
- IDAEA-CSIC, Jordi Girona 18, 08028 Barcelona, Spain
| | - Hervé Vezin
- LASIR
CNRS UMR 8516, Université Lille1, Sciences et Technologies, 59655 Villeneuve d’Ascq Cedex, France
| | - Ludovic Duponchel
- LASIR
CNRS UMR 8516, Université Lille1, Sciences et Technologies, 59655 Villeneuve d’Ascq Cedex, France
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Ambrose A, Cho BK. A Review of Technologies for Detection and Measurement of Adulterants in Cereals and Cereal Products. ACTA ACUST UNITED AC 2014. [DOI: 10.5307/jbe.2014.39.4.357] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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