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

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

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.

A novel aspect on different preservation methods for tomato paste by examining the protective effect of herb and spice oils

In this study, the effect of essential oils of herbs and spices on the protection of homemade tomato paste without additives was investigated. Garlic oil was used as plant essential oil and thyme oil was used as spice essential oil. Samples were kept without adding essential oils at the specified holding times in determined light and dark ambient conditions. After the test period of the prepared setups was completed, the amount of mold formed in the tomato paste was observed, and the optimum samples were determined as K4A4, K4K7, K4K13, K6K10, S6K4, S6K7, S6K10, S6A13 by weighing again and creating a %-time graph by mass. It was decided that thyme essential oil had a more protective effect in foods than garlic essential oil, with the analysis methods performed on optimum samples (Physical analysis, microbiological analysis, FT-IR, and TG DTA).

Rapid Identification of Insecticide- and Herbicide-Tolerant Genetically Modified Maize Using Mid-Infrared Spectroscopy

Genetically modified (GM) technology is of great significance for increasing crop production, protecting biodiversity, and reducing environmental pollution. However, with the frequent occurrence of safety events regarding GM foods, more and more disputes have arisen over the potential safety of transgenic technology. It is particularly necessary to find a fast and accurate method for transgenic product identification. In this research, mid-infrared spectroscopy, coupled with chemometric methods, was applied to discriminate GM maize from its non-GM parent. A total of 120 GM maize and 120 non-GM maize samples were prepared, and the spectral information in the range of 400–4000 cm−1 was collected. After acquiring the spectra, wavelet transform (WT) was used to preprocess the data, and k-means was carried out to split all samples into calibration and prediction sets in the ratio of 2:1. Principal component analysis (PCA) was then conducted to qualitatively distinguish the two types of samples, and an apparent cluster was observed. Since the full spectrum covered a large amount of data and redundant information, we adopted the successive projections algorithm (SPA) to select optimal wavelengths for further analysis. Chemometrics, including partial least squares-discriminant analysis (PLS-DA), the k-nearest neighbor algorithm (KNN), and the extreme learning machine (ELM), were performed to establish classification models based on full spectra and optimal wavelengths. The overall results indicated that ELM models based on full spectra and optimal spectra showed better accuracy and reliability, with a 100% recognition rate in the calibration set and a 98.75% recognition rate in the prediction set. It has been confirmed that mid-infrared spectroscopy, combined with chemometric methods, can be a novel approach to identify transgenic maize.

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

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.

Energy Harvesting Strategies for Wireless Sensor Networks and Mobile Devices: A Review

Wireless sensor network nodes and mobile devices are normally powered by batteries that, when depleted, must be recharged or replaced. This poses important problems, in particular for sensor nodes that are placed in inaccessible areas or biomedical sensors implanted in the human body where the battery replacement is very impractical. Moreover, the depleted battery must be properly disposed of in accordance with national and international regulations to prevent environmental pollution. A very interesting alternative to power mobile devices is energy harvesting where energy sources naturally present in the environment (such as sunlight, thermal gradients and vibrations) are scavenged to provide the power supply for sensor nodes and mobile systems. Since the presence of these energy sources is discontinuous in nature, electronic systems powered by energy harvesting must include a power management system and a storage device to store the scavenged energy. In this paper, the main strategies to design a wireless mobile sensor system powered by energy harvesting are reviewed and different sensor systems powered by such energy sources are presented.