Non-Destructive Quality Assessment of Tomato Paste by Using Portable Mid-Infrared Spectroscopy and Multivariate Analysis

Aykas, Didem Peren; Borba, Karla Rodrigues; Rodríguez-Saona, Luis E.

doi:10.3390/foods9091300

Cited by 12 publications

(2 citation statements)

References 55 publications

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“…This model is promising for robust and accurate tomato maturity grading in agricultural applications. Aykas et al [12] used a field-deployable portable infrared spectrometer for tomato paste detection. 1843 samples were collected from 2015-2019 in California, USA from four different leading tomato paste processors.…”

Section: Introductionmentioning

confidence: 99%

Tomato pest recognition using convolutional neural network in Bangladesh

Polin,

Hasan,

Habib

et al. 2024

Bulletin EEI

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The tomato is one of the most popular and well-liked veggies among Asians. It is interesting to note that in Bangladesh, it is the second most significant vegetable consumed. Moreover, tomato is served not only as a vegetable, but it is also served as sauce, jam, etc., and used in making different types of cuisines. But the fact is due to the pests, thousands of tons of tomatoes are harmed every year in Bangladesh. The production of tomatoes in Bangladesh is harmed by a number of dangerous pests. We develop a solution to recognize pests at an early stage. Five different pest types, including aphids, red spider mites, whiteflies, looper caterpillars, and thrips, have been studied in this research. To identify tomato pests, we curated image datasets from online and offline repositories and processed them using a convolutional neural network (CNN) model. We used features from CNN layers for three machine learning algorithms: Random Forest (RF), support vector machine (SVM), and K-Nearest Neighbors (K-NN). This comprehensive approach allowed a thorough comparison of these algorithms in tomato pest recognition. For recognizing tomato pests, our methods generate excellent results. The accuracy of our experiment is 95.49% which indicates the successful completion of the experiment.

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Section: Introductionmentioning

confidence: 99%

Tomato pest recognition using convolutional neural network in Bangladesh

Polin,

Hasan,

Habib

et al. 2024

Bulletin EEI

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“…In general, cherry tomatoes are mainly evaluated by testing the soluble solid content (SSC) and titratable acidity (TA), firmness and other indicators [2]. Acidity and sugar affect their taste comfort and can be used for grading internal quality; changes in firmness can be used for quality monitoring during storage and transportation of cherry tomatoes and for determining the ripeness of the fruit [3]. Traditionally, the SSC and TA of cherry tomatoes are tested by the refractometer method, which requires the fruit to be extruded into juice and then titrated, and the firmness is tested with the fruit firmness tester.…”

Section: Introductionmentioning

confidence: 99%

Combining Vis-NIR and NIR Spectral Imaging Techniques with Data Fusion for Rapid and Nondestructive Multi-Quality Detection of Cherry Tomatoes

Tan,

Mo,

Ruan

et al. 2023

Foods

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Firmness, soluble solid content (SSC) and titratable acidity (TA) are characteristic substances for evaluating the quality of cherry tomatoes. In this paper, a hyper spectral imaging (HSI) system using visible/near-infrared (Vis-NIR) and near-infrared (NIR) was proposed to detect the key qualities of cherry tomatoes. The effects of individual spectral information and fused spectral information in the detection of different qualities were compared for firmness, SSC and TA of cherry tomatoes. Data layer fusion combined with multiple machine learning methods including principal component regression (PCR), partial least squares regression (PLSR), support vector regression (SVR) and back propagation neural network (BP) is used for model training. The results show that for firmness, SSC and TA, the determination coefficient R2 of the multi-quality prediction model established by Vis-NIR spectra is higher than that of NIR spectra. The R2 of the best model obtained by SSC and TA fusion band is greater than 0.9, and that of the best model obtained by the firmness fusion band is greater than 0.85. It is better to use the spectral bands after information fusion for nondestructive quality detection of cherry tomatoes. This study shows that hyperspectral imaging technology can be used for the nondestructive detection of multiple qualities of cherry tomatoes, and the method based on the fusion of two spectra has a better prediction effect for the rapid detection of multiple qualities of cherry tomatoes compared with a single spectrum. This study can provide certain technical support for the rapid nondestructive detection of multiple qualities in other melons and fruits.

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Non-destructive assessment of vitamin C in foods: a review of the main findings and limitations of vibrational spectroscopic techniques

Tirado-Kulieva¹,

Hernández-Martínez

Suomela

2022

Eur Food Res Technol

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The constant increase in the demand for safe and high-quality food has generated the need to develop efficient methods to evaluate food composition, vitamin C being one of the main quality indicators. However, its heterogeneity and susceptibility to degradation makes the analysis of vitamin C difficult by conventional techniques, but as a result of technological advances, vibrational spectroscopy techniques have been developed that are more efficient, economical, fast, and non-destructive. This review focuses on main findings on the evaluation of vitamin C in foods by using vibrational spectroscopic techniques. First, the fundamentals of ultraviolet–visible, infrared and Raman spectroscopy are detailed. Also, chemometric methods, whose use is essential for a correct processing and evaluation of the spectral information, are described. The use and importance of vibrational spectroscopy in the evaluation of vitamin C through qualitative characterization and quantitative analysis is reported. Finally, some limitations of the techniques and potential solutions are described, as well as future trends related to the utilization of vibrational spectroscopic techniques.

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Non-Destructive Quality Assessment of Tomato Paste by Using Portable Mid-Infrared Spectroscopy and Multivariate Analysis

Cited by 12 publications

References 55 publications

Tomato pest recognition using convolutional neural network in Bangladesh

Tomato pest recognition using convolutional neural network in Bangladesh

Combining Vis-NIR and NIR Spectral Imaging Techniques with Data Fusion for Rapid and Nondestructive Multi-Quality Detection of Cherry Tomatoes

Non-destructive assessment of vitamin C in foods: a review of the main findings and limitations of vibrational spectroscopic techniques

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