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Tosin R, Cunha M, Monteiro-Silva F, Santos F, Barroso T, Martins R. Bi-directional hyperspectral reconstruction of cherry tomato: diagnosis of internal tissues maturation stage and composition. FRONTIERS IN PLANT SCIENCE 2024; 15:1351958. [PMID: 38434432 PMCID: PMC10905776 DOI: 10.3389/fpls.2024.1351958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 01/24/2024] [Indexed: 03/05/2024]
Abstract
Introduction Precision monitoring maturity in climacteric fruits like tomato is crucial for minimising losses within the food supply chain and enhancing pre- and post-harvest production and utilisation. Objectives This paper introduces an approach to analyse the precision maturation of tomato using hyperspectral tomography-like. Methods A novel bi-directional spectral reconstruction method is presented, leveraging visible to near-infrared (Vis-NIR) information gathered from tomato spectra and their internal tissues (skin, pulp, and seeds). The study, encompassing 118 tomatoes at various maturation stages, employs a multi-block hierarchical principal component analysis combined with partial least squares for bi-directional reconstruction. The approach involves predicting internal tissue spectra by decomposing the overall tomato spectral information, creating a superset with eight latent variables for each tissue. The reverse process also utilises eight latent variables for reconstructing skin, pulp, and seed spectral data. Results The reconstruction of the tomato spectra presents a mean absolute percentage error of 30.44 % and 5.37 %, 5.25 % and 6.42 % and Pearson's correlation coefficient of 0.85, 0.98, 0.99 and 0.99 for the skin, pulp and seed, respectively. Quality parameters, including soluble solid content (%), chlorophyll (a.u.), lycopene (a.u.), and puncture force (N), were assessed and modelled with PLS with the original and reconstructed datasets, presenting a range of R2 higher than 0.84 in the reconstructed dataset. An empirical demonstration of the tomato maturation in the internal tissues revealed the dynamic of the chlorophyll and lycopene in the different tissues during the maturation process. Conclusion The proposed approach for inner tomato tissue spectral inference is highly reliable, provides early indications and is easy to operate. This study highlights the potential of Vis-NIR devices in precision fruit maturation assessment, surpassing conventional labour-intensive techniques in cost-effectiveness and efficiency. The implications of this advancement extend to various agronomic and food chain applications, promising substantial improvements in monitoring and enhancing fruit quality.
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Affiliation(s)
- Renan Tosin
- Department of Geosciences, Environment and Spatial Planning, Faculty of Sciences of the University of Porto, Porto, Portugal
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Universidade do Porto, Porto, Portugal
| | - Mario Cunha
- Department of Geosciences, Environment and Spatial Planning, Faculty of Sciences of the University of Porto, Porto, Portugal
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Universidade do Porto, Porto, Portugal
| | - Filipe Monteiro-Silva
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Universidade do Porto, Porto, Portugal
| | - Filipe Santos
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Universidade do Porto, Porto, Portugal
| | - Teresa Barroso
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Universidade do Porto, Porto, Portugal
| | - Rui Martins
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Universidade do Porto, Porto, Portugal
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Li S, Li J, Wang Q, Shi R, Yang X, Zhang Q. Determination of soluble solids content of multiple varieties of tomatoes by full transmission visible-near infrared spectroscopy. FRONTIERS IN PLANT SCIENCE 2024; 15:1324753. [PMID: 38322826 PMCID: PMC10844474 DOI: 10.3389/fpls.2024.1324753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 01/08/2024] [Indexed: 02/08/2024]
Abstract
Introduction Soluble solids content (SSC) is a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming. Methods To enhance accuracy and efficiency in SSC assessment, this study employs full transmission visible and near-infrared (Vis-NIR) spectroscopy and multi-point spectral data collection techniques to quantitatively analyze SSC in two tomato varieties ('Provence' and 'Jingcai No.8' tomatoes). Preprocessing of the multi-point spectra is carried out using a weighted averaging approach, aimed at noise reduction, signal-to-noise ratio improvement, and overall data quality enhancement. Taking into account the potential influence of various detection orientations and preprocessing methods on model outcomes, we investigate the combination of partial least squares regression (PLSR) with two orientations (O1 and O2) and two preprocessing techniques (Savitzky-Golay smoothing (SG) and Standard Normal Variate transformation (SNV)) in the development of SSC prediction models. Results The model achieved the best results in the O2 orientation and SNV pretreatment as follows: 'Provence' tomato (Rp = 0.81, RMSEP = 0.69°Brix) and 'Jingcai No.8' tomatoes (Rp = 0.84, RMSEP = 0.64°Brix). To further optimize the model, characteristic wavelength selection is introduced through Least Angle Regression (LARS) with L1 and L2 regularization. Notably, when λ=0.004, LARS-L1 produces superior results ('Provence' tomato: Rp = 0.95, RMSEP = 0.35°Brix; 'Jingcai No.8' tomato: Rp = 0.96, RMSEP = 0.33°Brix). Discussion This study underscores the effectiveness of full transmission Vis-NIR spectroscopy in predicting SSC in different tomato varieties, offering a viable method for accurate and swift SSC assessment in tomatoes.
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Affiliation(s)
- Sheng Li
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi, China
| | - Jiangbo Li
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Qingyan Wang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Ruiyao Shi
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Xuhai Yang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi, China
| | - Qian Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi, China
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Duckena L, Alksnis R, Erdberga I, Alsina I, Dubova L, Duma M. Non-Destructive Quality Evaluation of 80 Tomato Varieties Using Vis-NIR Spectroscopy. Foods 2023; 12:foods12101990. [PMID: 37238808 DOI: 10.3390/foods12101990] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/07/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
Traditional biochemical methods are resource- and time-consuming; therefore, there is a need for cost-effective alternatives. A spectral analysis is one of the non-destructive techniques that are more widely used for fruit quality determination; however, references are needed for traditional methods. In this study, visible and near-infrared (Vis-NIR) spectroscopy was used to analyze the internal quality attributes of tomatoes. For the first time, 80 varieties with large differences in fruit size, shape, color, and internal structure were used for an analysis. The aim of this study was to develop models suitable to predict a taste index, as well as the content of lycopene, flavonoids, β-carotene, total phenols, and dry matter of intact tomatoes based on Vis-NIR reflectance spectra. The content of phytochemicals was determined in 80 varieties of tomatoes. A total of 140 Vis-NIR reflectance spectra were obtained using the portable spectroradiometer RS-3500 (Spectral Evolution Inc.). Partial least squares regression (PLS) and multiple scatter correction (MSC) were used to develop calibration models. Our results indicated that PLS models with good prediction accuracies were obtained. The present study showed the high capability of Vis-NIR spectroscopy to determine the content of lycopene and dry matter of intact tomatoes with a determination coefficient of 0.90 for both parameters. A regression fit of R2 = 0.86, R2 = 0.84, R2 = 0.82, and R2= 0.73 was also achieved for the taste index, flavonoids, β-carotene, and total phenols, respectively.
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Affiliation(s)
- Lilija Duckena
- Faculty of Agriculture, Institute of Soil and Plant Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Reinis Alksnis
- Department of Mathematics, Faculty of Information Technologies, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Ieva Erdberga
- Faculty of Agriculture, Institute of Soil and Plant Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Ina Alsina
- Faculty of Agriculture, Institute of Soil and Plant Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Laila Dubova
- Faculty of Agriculture, Institute of Soil and Plant Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Mara Duma
- Department of Chemistry, Faculty of Food Technology, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
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Égei M, Takács S, Palotás G, Palotás G, Szuvandzsiev P, Daood HG, Helyes L, Pék Z. Prediction of Soluble Solids and Lycopene Content of Processing Tomato Cultivars by Vis-NIR Spectroscopy. Front Nutr 2022; 9:845317. [PMID: 35836590 PMCID: PMC9274195 DOI: 10.3389/fnut.2022.845317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 06/02/2022] [Indexed: 11/25/2022] Open
Abstract
Tomato-based products are significant components of vegetable consumption. The processing tomato industry is unquestionably in need of a rapid definition method for measuring soluble solids content (SSC) and lycopene content. The objective was to find the best chemometric method for the estimation of SSC and lycopene content from visible and near-infrared (Vis-NIR) absorbance and reflectance data so that they could be determined without the use of chemicals in the process. A total of 326 Vis-NIR absorbance and reflectance spectra and reference measurements were available to calibrate and validate prediction models. The obtained spectra can be manipulated using different preprocessing methods and multivariate data analysis techniques to develop prediction models for these two main quality attributes of tomato fruits. Eight different method combinations were compared in homogenized and intact fruit samples. For SSC prediction, the results showed that the best root mean squared error of cross-validation (RMSECV) originated from raw absorbance (0.58) data and with multiplicative scatter correction (MSC) (0.59) of intact fruit in Vis-NIR, and first derivatives of reflectance (R2 = 0.41) for homogenate in the short-wave infrared (SWIR) region. The best predictive ability for lycopene content of homogenate in the SWIR range (R2 = 0.47; RMSECV = 17.95 mg kg–1) was slightly lower than that of Vis-NIR (R2 = 0.68; 15.07 mg kg–1). This study reports the suitability of two Vis-NIR spectrometers, absorbance/reflectance spectra, preprocessing methods, and partial least square (PLS) regression to predict SSC and lycopene content of intact tomato fruit and its homogenate.
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Affiliation(s)
- Márton Égei
- Institute of Horticultural Science, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
| | - Sándor Takács
- Institute of Horticultural Science, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
| | | | | | | | - Hussein Gehad Daood
- Regional Knowledge Center, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
| | - Lajos Helyes
- Institute of Horticultural Science, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
| | - Zoltán Pék
- Institute of Horticultural Science, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
- *Correspondence: Zoltán Pék,
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Li L, Lu L, Zhao X, Hu D, Tang T, Tang Y. Nondestructive detection of tomato quality based on multiregion combination model. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.14100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Li Li
- School of Physics Guizhou University Guiyang China
| | - Li‐Min Lu
- School of Physics Guizhou University Guiyang China
| | | | - De‐Yuan Hu
- School of Physics Guizhou University Guiyang China
| | - Tian‐Yu Tang
- School of Physics Guizhou University Guiyang China
| | - Yan‐Lin Tang
- School of Physics Guizhou University Guiyang China
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Abu-Khalaf N, Masoud W. Electronic Nose for Differentiation and Quantification of Yeast Species in White Fresh Soft Cheese. Appl Bionics Biomech 2022; 2022:8472661. [PMID: 35082918 PMCID: PMC8786551 DOI: 10.1155/2022/8472661] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 12/16/2021] [Accepted: 12/27/2021] [Indexed: 11/18/2022] Open
Abstract
Detection of food spoilage with simple and fast methods is an important issue in food security and safety. The present study is mainly aimed at identifying and quantifying four yeast species in white fresh soft cheese using an electronic nose (EN). The yeast species Pichia anomala, Pichia kluyveri, Hanseniaspora uvarum, and Debaryomyces hansenii were used. Six concentrations of each yeast species (100, 200, 400, 600, 800, and 1000 cells/g cheese) were inoculated in 100 g of fresh soft cheese and incubated for 48 h at 25°C. The EN was used to identify and quantify different yeast species in cheese samples. It was found that EN was able to discriminate between four yeast species using principal component analysis (PCA). Moreover, EN was able to quantify in good precision three (Pichia anomala, Pichia kluyveri, and Debaryomyces hansenii) of the four tested yeasts presented in cheese samples using partial least squares (PLS) models. It seems that EN is a reliable tool that can be used as a fast technique to identify and quantify cheese spoilage in the cheese industry.
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Affiliation(s)
- Nawaf Abu-Khalaf
- Department of Agricultural Biotechnology, Faculty of Agricultural Sciences and Technology, Palestine Technical University-Kadoorie (PTUK), P.O. Box 7, Jaffa Street, Tulkarm, State of Palestine
| | - Wafa Masoud
- Department of Agricultural Biotechnology, Faculty of Agricultural Sciences and Technology, Palestine Technical University-Kadoorie (PTUK), P.O. Box 7, Jaffa Street, Tulkarm, State of Palestine
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