1
|
Aline U, Bhattacharya T, Faqeerzada MA, Kim MS, Baek I, Cho BK. Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review. FRONTIERS IN PLANT SCIENCE 2023; 14:1240361. [PMID: 37662162 PMCID: PMC10471194 DOI: 10.3389/fpls.2023.1240361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 07/27/2023] [Indexed: 09/05/2023]
Abstract
The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.
Collapse
Affiliation(s)
- Umuhoza Aline
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
| | - Tanima Bhattacharya
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
| | | | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Byoung-Kwan Cho
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
- Department of Smart Agricultural Systems, Chungnam National University, Daejeon, Republic of Korea
| |
Collapse
|
2
|
Baran C, Sharma S, Tripathi A, Awasthi A, Jaiswal A, Tandon P, Singh R, Uttam KN. Non-Destructive Monitoring of Ripening Process of the Underutilized Fruit Kadam Using Laser-Induced Fluorescence and Confocal Micro Raman Spectroscopy. ANAL LETT 2022. [DOI: 10.1080/00032719.2022.2137523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Chhavi Baran
- Centre for Environmental Science, IIDS, University of Allahabad, Allahabad, India
| | - Sweta Sharma
- Saha’s Spectroscopy Laboratory, Department of Physics, University of Allahabad, Allahabad, India
- Department of Applied Science and Humanities, Faculty of Engineering and Technology, Khwaja Moinuddin Chishti Language University, Lucknow, India
| | - Aradhana Tripathi
- Saha’s Spectroscopy Laboratory, Department of Physics, University of Allahabad, Allahabad, India
| | - Aishwary Awasthi
- Saha’s Spectroscopy Laboratory, Department of Physics, University of Allahabad, Allahabad, India
| | - Aarti Jaiswal
- Centre for Material Sciences, IIDS, University of Allahabad, Allahabad, India
| | | | - Renu Singh
- School of Humanities and Sciences, Malla Reddy University, Hyderabad, India
| | - K. N. Uttam
- Saha’s Spectroscopy Laboratory, Department of Physics, University of Allahabad, Allahabad, India
| |
Collapse
|
3
|
Ghanei Ghooshkhaneh N, Golzarian MR, Mamarabadi M. Spectral pattern study of citrus black rot caused by Alternaria alternata and selecting optimal wavelengths for decay detection. Food Sci Nutr 2022; 10:1694-1706. [PMID: 35702301 PMCID: PMC10153684 DOI: 10.1002/fsn3.2739] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 01/07/2022] [Indexed: 11/16/2022] Open
Abstract
Fungal decay is one of the most common diseases that affect postharvest operations and sales of citrus. Sometimes, fungal disease develops and spreads inside the fruit and in the advanced stages of the disease, it appears apparent, so the use of efficient and reliable methods for early detection of the disease is very important. In this study, early detection of citrus black rot disease caused by Alternaria genus fungus was examined using spectroscopy. Jaffa oranges were inoculated with Alternaria alternata. The samples were inspected by spectroscopy (200–1100 nm) in the 1st, 2nd, and 3rd weeks after inoculation. The classification of healthy and infected samples and selection of most important wavelengths were conducted by soft independent modeling of class analogy (SIMCA). The most important wavelengths in the detection of healthy and infected samples of the 1st week were 507, 933, 937, and 950 nm with a classification accuracy of 60%. The most important wavelengths of the 2nd week were 522 and 787 nm with a classification accuracy of 60%. Also, wavelengths of 546, 660, 691, and 839 were found to be effective in the 3rd week with a classification accuracy of 100%.
Collapse
Affiliation(s)
| | | | - Mojtaba Mamarabadi
- Department of Plant Protection Ferdowsi University of Mashhad Mashhad Iran
| |
Collapse
|
4
|
Automatic Classification of the Ripeness Stage of Mango Fruit Using a Machine Learning Approach. AGRIENGINEERING 2022. [DOI: 10.3390/agriengineering4010003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Most mango farms classify the maturity stage manually by trained workers using external indicators such as size, shape, and skin color, which can lead to human error or inconsistencies. We developed four common machine learning (ML) classifiers, the k-mean, naïve Bayes, support vector machine, and feed-forward artificial neural network (FANN), all of which were aimed at classifying the ripeness stage of mangoes at harvest. The ML classifiers were trained on biochemical data and then tested on physical and electrical data.The performance of the ML models was compared using fourfold cross validation. The FANN classifier performed the best, with a mean accuracy of 89.6% for unripe, ripe, and overripe classes, when compared to the other classifiers.
Collapse
|
5
|
Ibrahim A, Alghannam A, Eissa A, Firtha F, Kaszab T, Kovacs Z, Helyes L. Preliminary Study for Inspecting Moisture Content, Dry Matter Content, and Firmness Parameters of Two Date Cultivars Using an NIR Hyperspectral Imaging System. Front Bioeng Biotechnol 2021; 9:720630. [PMID: 34746101 PMCID: PMC8570186 DOI: 10.3389/fbioe.2021.720630] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 08/13/2021] [Indexed: 11/13/2022] Open
Abstract
The assessment and assurance of the quality attributes of dates is a key factor in increasing the competitiveness and consumer acceptance of this fruit. The increasing demand for date fruits requires a rapid and automated method for monitoring and analyzing the quality attributes of date fruits to replace the conventional methods used by inspection which limits the production and involves human errors. Moisture content (MC), dry matter content (DMC), and firmness (F) are three important quality attributes for two date cultivars (Khalas and Sukkari) that have been inspected using the hyperspectral imaging (HSI) technique based on the reflectance mode. Images of intact date fruits at the maturity stage Tamr were obtained within the wavelength range of 950–1750 nm. Monitoring and assessment of MC, DMC, and F [first maximum rupture force (MF, N)] were performed using a partial least squares regression model. Accurate prediction models were attained. The results highlight that the coefficients of determination (R2Prediction) are estimated to be 0.91 and 0.89 for MC, DMC, and F (N) with the lowest values of the standard error of prediction (SEP) equal to 0.82, 0.81 (%), and 4.12 (N), respectively, and the residual predictive deviation (RPD) values were 3.65, 3.69, and 3.42 for MC, DMC, and F (N), respectively. The results obtained from this preliminary study indicate the great potential of applying HSI for the assessment of physical, chemical, and sensory quality attributes of date fruits overall in the five maturity stages.
Collapse
Affiliation(s)
- Ayman Ibrahim
- Agricultural Engineering Research Institute (AEnRI), Agricultural Research Center (ARC), Giza, Egypt
| | - Abdulrahman Alghannam
- Department of Agricultural Systems Engineering, College of Agricultural and Food Sciences, King Faisal University, Al-Hassa, Saudi Arabia
| | - Ayman Eissa
- Department of Agricultural Engineering, Faculty of Agriculture, Menoufia University, Shebin El Koum, Egypt
| | - Ferenc Firtha
- Department of Measurements and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, Budapest, Hungary
| | - Timea Kaszab
- Department of Measurements and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, Budapest, Hungary
| | - Zoltan Kovacs
- Department of Measurements and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, Budapest, Hungary
| | - Lajos Helyes
- Horticultural institute, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
| |
Collapse
|
6
|
Munawar AA, Kusumiyati, Wahyuni D. Near infrared spectroscopic data for rapid and simultaneous prediction of quality attributes in intact mango fruits. Data Brief 2019; 27:104789. [PMID: 31788517 PMCID: PMC6880090 DOI: 10.1016/j.dib.2019.104789] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 11/01/2019] [Accepted: 11/04/2019] [Indexed: 01/18/2023] Open
Abstract
Presented dataset contains spectral data on near infrared region for a total of 186 intact mango fruit samples from 4 different cultivars (cv. Kweni, Cengkir, Palmer and Kent). Near infrared spectral data were collected and recorded as absorbance (Log(1/R)) data in wavelength range of 1000–2500 nm. Those spectral data are potential to be re-used and analysed for the prediction of mango quality attributes in form of vitamin C, soluble solids content (SSC) and total acidity (TA). Spectra data can be corrected and enhanced using several algorithms such as multiplicative scatter correction (MSC) and de-trending (DT). Prediction models can be established using common regression approach like partial least square regression (PLSR).
Collapse
Affiliation(s)
- Agus Arip Munawar
- Department of Agricultural Engineering, Syiah Kuala University, Banda Aceh Indonesia
| | - Kusumiyati
- Department of Agronomy, Padjadjaran University, Bandung Indonesia
| | - Devi Wahyuni
- Department of Agricultural Engineering, Syiah Kuala University, Banda Aceh Indonesia
| |
Collapse
|
7
|
Rapid non-destructive moisture content monitoring using a handheld portable Vis–NIR spectrophotometer during solar drying of mangoes (Mangifera indica L.). JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2019. [DOI: 10.1007/s11694-019-00327-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
|
8
|
Potential of Near-Infrared (NIR) Spectroscopy and Hyperspectral Imaging for Quality and Safety Assessment of Fruits: an Overview. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01609-1] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
|
9
|
Yeong TJ, Pin Jern K, Yao LK, Hannan MA, Hoon STG. Applications of Photonics in Agriculture Sector: A Review. Molecules 2019; 24:E2025. [PMID: 31137897 PMCID: PMC6571790 DOI: 10.3390/molecules24102025] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 05/10/2019] [Accepted: 05/12/2019] [Indexed: 11/17/2022] Open
Abstract
The agricultural industry has made a tremendous contribution to the foundations of civilization. Basic essentials such as food, beverages, clothes and domestic materials are enriched by the agricultural industry. However, the traditional method in agriculture cultivation is labor-intensive and inadequate to meet the accelerating nature of human demands. This scenario raises the need to explore state-of-the-art crop cultivation and harvesting technologies. In this regard, optics and photonics technologies have proven to be effective solutions. This paper aims to present a comprehensive review of three photonic techniques, namely imaging, spectroscopy and spectral imaging, in a comparative manner for agriculture applications. Essentially, the spectral imaging technique is a robust solution which combines the benefits of both imaging and spectroscopy but faces the risk of underutilization. This review also comprehends the practicality of all three techniques by presenting existing examples in agricultural applications. Furthermore, the potential of these techniques is reviewed and critiqued by looking into agricultural activities involving palm oil, rubber, and agro-food crops. All the possible issues and challenges in implementing the photonic techniques in agriculture are given prominence with a few selective recommendations. The highlighted insights in this review will hopefully lead to an increased effort in the development of photonics applications for the future agricultural industry.
Collapse
Affiliation(s)
- Tan Jin Yeong
- Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.
| | - Ker Pin Jern
- Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.
| | - Lau Kuen Yao
- Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.
| | - M A Hannan
- Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.
| | - Shirley Tang Gee Hoon
- Microbiology Unit, Department of Pre-clinical, International Medical School, Management and Science University, University Drive, Off Persiaran Olahraga, Seksyen 13, Shah Alam 40100, Selangor, Malaysia.
| |
Collapse
|
10
|
Xu D, Wang H, Ji H, Zhang X, Wang Y, Zhang Z, Zheng H. Hyperspectral Imaging for Evaluating Impact Damage to Mango According to Changes in Quality Attributes. SENSORS 2018; 18:s18113920. [PMID: 30441764 PMCID: PMC6275074 DOI: 10.3390/s18113920] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Revised: 10/28/2018] [Accepted: 11/10/2018] [Indexed: 11/26/2022]
Abstract
Evaluation of impact damage to mango (Mangifera indica Linn) as a result of dropping from three different heights, namely, 0.5, 1.0 and 1.5 m, was conducted by hyperspectral imaging (HSI). Reflectance spectra in the 900–1700 nm region were used to develop prediction models for pulp firmness (PF), total soluble solids (TSS), titratable acidity (TA) and chroma (∆b*) by a partial least squares (PLS) regression algorithm. The results showed that the changes in the mangoes’ quality attributes, which were also reflected in the spectra, had a strong relationship with dropping height. The best predictive performance measured by coefficient of determination (R2) and root mean square errors of prediction (RMSEP) values were: 0.84 and 31.6 g for PF, 0.9 and 0.49 oBrix for TSS, 0.65 and 0.1% for TA, 0.94 and 0.96 for chroma, respectively. Classification of the degree of impact damage to mango achieved an accuracy of more than 77.8% according to ripening index (RPI). The results show the potential of HSI to evaluate impact damage to mango by combining with changes in quality attributes.
Collapse
Affiliation(s)
- Duohua Xu
- Tianjin Key Laboratory of Refrigeration Technology, Tianjin University of Commerce, Tianjin 300134, China.
| | - Huaiwen Wang
- Tianjin Key Laboratory of Refrigeration Technology, Tianjin University of Commerce, Tianjin 300134, China.
- School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China.
| | - Hongwei Ji
- Tianjin Key Laboratory of Refrigeration Technology, Tianjin University of Commerce, Tianjin 300134, China.
| | - Xiaochuan Zhang
- Tianjin Key Laboratory of Refrigeration Technology, Tianjin University of Commerce, Tianjin 300134, China.
| | - Yanan Wang
- School of Engineering, Deakin University, Waurn Ponds campus, Geelong, Victoria 3216, Australia.
| | - Zhe Zhang
- Tianjin Key Laboratory of Refrigeration Technology, Tianjin University of Commerce, Tianjin 300134, China.
| | - Hongfei Zheng
- Tianjin Key Laboratory of Refrigeration Technology, Tianjin University of Commerce, Tianjin 300134, China.
| |
Collapse
|
11
|
Santos Neto JPD, Leite GWP, Oliveira GDS, Cunha Júnior LC, Gratão PL, Morais CDLMD, Teixeira GHDA. Cold storage of ‘Palmer’ mangoes sorted based on dry matter content using portable near infrared (VIS-NIR) spectrometer. J FOOD PROCESS PRES 2018. [DOI: 10.1111/jfpp.13644] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- João Paixão dos Santos Neto
- Departamento de Produção Vegetal, Faculdade de Ciências Agrárias e Veterinárias (FCAV), Universidade Estadual Paulista (UNESP); Campus de Jaboticabal. Via de acesso Prof. Paulo Donato Castellane s/n; Jaboticabal SP, CEP: 14.870-900 Brazil
| | - Gustavo Walace Pacheco Leite
- Departamento de Produção Vegetal, Faculdade de Ciências Agrárias e Veterinárias (FCAV), Universidade Estadual Paulista (UNESP); Campus de Jaboticabal. Via de acesso Prof. Paulo Donato Castellane s/n; Jaboticabal SP, CEP: 14.870-900 Brazil
| | - Gabriele da Silva Oliveira
- Departamento de Produção Vegetal, Faculdade de Ciências Agrárias e Veterinárias (FCAV), Universidade Estadual Paulista (UNESP); Campus de Jaboticabal. Via de acesso Prof. Paulo Donato Castellane s/n; Jaboticabal SP, CEP: 14.870-900 Brazil
| | - Luís Carlos Cunha Júnior
- Departamento de Horticultura, Escola de Agronomia (EA), Setor de Horticultura. Rodovia Goiânia Nova Veneza, Universidade Federal de Goiás (UFG), km 0, Campus Samambaia, Caixa Postal 131; Goiânia GO, CEP: 74.690-900 Brazil
| | - Priscila Lupino Gratão
- Departamento de Produção Vegetal, Faculdade de Ciências Agrárias e Veterinárias (FCAV), Universidade Estadual Paulista (UNESP); Campus de Jaboticabal. Via de acesso Prof. Paulo Donato Castellane s/n; Jaboticabal SP, CEP: 14.870-900 Brazil
| | | | - Gustavo Henrique de Almeida Teixeira
- Departamento de Produção Vegetal, Faculdade de Ciências Agrárias e Veterinárias (FCAV), Universidade Estadual Paulista (UNESP); Campus de Jaboticabal. Via de acesso Prof. Paulo Donato Castellane s/n; Jaboticabal SP, CEP: 14.870-900 Brazil
| |
Collapse
|
12
|
Li B, Lecourt J, Bishop G. Advances in Non-Destructive Early Assessment of Fruit Ripeness towards Defining Optimal Time of Harvest and Yield Prediction-A Review. PLANTS (BASEL, SWITZERLAND) 2018; 7:E3. [PMID: 29320410 PMCID: PMC5874592 DOI: 10.3390/plants7010003] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 01/05/2018] [Accepted: 01/08/2018] [Indexed: 11/17/2022]
Abstract
Global food security for the increasing world population not only requires increased sustainable production of food but a significant reduction in pre- and post-harvest waste. The timing of when a fruit is harvested is critical for reducing waste along the supply chain and increasing fruit quality for consumers. The early in-field assessment of fruit ripeness and prediction of the harvest date and yield by non-destructive technologies have the potential to revolutionize farming practices and enable the consumer to eat the tastiest and freshest fruit possible. A variety of non-destructive techniques have been applied to estimate the ripeness or maturity but not all of them are applicable for in situ (field or glasshouse) assessment. This review focuses on the non-destructive methods which are promising for, or have already been applied to, the pre-harvest in-field measurements including colorimetry, visible imaging, spectroscopy and spectroscopic imaging. Machine learning and regression models used in assessing ripeness are also discussed.
Collapse
Affiliation(s)
- Bo Li
- NIAB EMR, East Malling, Kent ME19 6BJ, UK.
| | | | | |
Collapse
|
13
|
Detection and quantification of anionic detergent (lissapol) in milk using attenuated total reflectance-Fourier Transform Infrared spectroscopy. Food Chem 2017; 221:815-821. [DOI: 10.1016/j.foodchem.2016.11.095] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Revised: 07/27/2016] [Accepted: 11/21/2016] [Indexed: 11/23/2022]
|
14
|
|
15
|
Rapid and non-destructive determination of quality parameters in the ‘Tommy Atkins’ mango using a novel handheld near infrared spectrometer. Food Chem 2016; 197 Pt B:1207-14. [DOI: 10.1016/j.foodchem.2015.11.080] [Citation(s) in RCA: 101] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Revised: 11/09/2015] [Accepted: 11/14/2015] [Indexed: 11/20/2022]
|
16
|
Lorente D, Escandell-Montero P, Cubero S, Gómez-Sanchis J, Blasco J. Visible–NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit. J FOOD ENG 2015. [DOI: 10.1016/j.jfoodeng.2015.04.010] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
17
|
Liu C, Yang SX, Deng L. Determination of internal qualities of Newhall navel oranges based on NIR spectroscopy using machine learning. J FOOD ENG 2015. [DOI: 10.1016/j.jfoodeng.2015.03.022] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
18
|
Fruit quality evaluation using spectroscopy technology: a review. SENSORS 2015; 15:11889-927. [PMID: 26007736 PMCID: PMC4481958 DOI: 10.3390/s150511889] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Revised: 05/14/2015] [Accepted: 05/18/2015] [Indexed: 11/26/2022]
Abstract
An overview is presented with regard to applications of visible and near infrared (Vis/NIR) spectroscopy, multispectral imaging and hyperspectral imaging techniques for quality attributes measurement and variety discrimination of various fruit species, i.e., apple, orange, kiwifruit, peach, grape, strawberry, grape, jujube, banana, mango and others. Some commonly utilized chemometrics including pretreatment methods, variable selection methods, discriminant methods and calibration methods are briefly introduced. The comprehensive review of applications, which concentrates primarily on Vis/NIR spectroscopy, are arranged according to fruit species. Most of the applications are focused on variety discrimination or the measurement of soluble solids content (SSC), acidity and firmness, but also some measurements involving dry matter, vitamin C, polyphenols and pigments have been reported. The feasibility of different spectral modes, i.e., reflectance, interactance and transmittance, are discussed. Optimal variable selection methods and calibration methods for measuring different attributes of different fruit species are addressed. Special attention is paid to sample preparation and the influence of the environment. Areas where further investigation is needed and problems concerning model robustness and model transfer are identified.
Collapse
|
19
|
Srivichien S, Terdwongworakul A, Teerachaichayut S. Quantitative prediction of nitrate level in intact pineapple using Vis–NIRS. J FOOD ENG 2015. [DOI: 10.1016/j.jfoodeng.2014.11.004] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
20
|
Zhang L, Xu H, Gu M. Use of signal to noise ratio and area change rate of spectra to evaluate the Visible/NIR spectral system for fruit internal quality detection. J FOOD ENG 2014. [DOI: 10.1016/j.jfoodeng.2014.04.009] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
21
|
Nakariyakul S. Internal damage inspection of almond nuts using optimal near-infrared waveband selection technique. J FOOD ENG 2014. [DOI: 10.1016/j.jfoodeng.2013.11.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|