Development of ANN models based on combined UV-vis-NIR spectra for rapid quantification of physical and chemical properties of industrial hemp extracts

Neural network enhanced aging time measurements of diary product remaining with infrared spectroscopy

The determination of the drying degree of food residues on surfaces is an important step before efficient cleaning can be achieved. To accomplish this goal, a rapid evaluation based on a neural network and non-invasive measurement technique is introduced. Two common starch-based products and various yogurts from different manufacturers are used as example contaminants to determine the aging time of dried food residue. Near-infrared spectroscopy serves as a modern and fast measurement technique for investigating food compositions. Two analysis methods were compared for processing the measured near-infrared spectral data. The raw data were analyzed using partial least squares regression in conjunction with necessary preprocessing steps. As an alternative method, three different types of neural networks are employed. The aim of this approach is to compensate for the filtering steps before regression, which are typically necessary for multivariate regression. The challenge is to measure three different types of food and obtain a reliable prediction of moisture content in order to draw conclusions about the drying time. The experiments have shown that simple flat neural networks have similar accuracy compared to conventional regression. The use of a convolutional layer in advance demonstrates a significant improvement in prediction compared to other neural networks and even manages to surpass the accuracy of PLS regression. A network with a convolutional layer can also compensate for the sometimes strong variations between food types.

Classification of Rice Varieties Using SIMCA Applied to NIR Spectroscopic Data

The selection of suitable rice varieties is the key to achieve high and stable yields, and the correct identification of rice varieties is the prerequisite for seed selection. In this paper, with Kenjing No.5, No.6, and No.9 as the subjects, the effectiveness of near-infrared spectroscopy (NIRS) combined with soft independent modeling of class analogy (SIMCA) in the rapid identification of rice varieties was explored. The modeling sets of Kenjing No.5, No.6, and No.9 samples were respectively used to establish a SIMCA classification model based on principal component analysis (PCA). The accuracies of the model in classifying the rice samples in the modeling set were 100, 100, and 97.5%, respectively. Then, the established SIMCA model was used to identify the rice samples in the test set. According to the experimental findings, the SIMCA analytical method achieved 100% prediction accuracy for the Kenjing No.5, Kenjing No.6, and Hongyu 001-1 samples. For the Kenjing No.9 sample, the accuracy rate was 90% with a 10% sample of Kenjing No.9 misidentified as Kenjing No.6. Therefore, the analytical method of NIRS combined with SIMCA could effectively identify the rice varieties, providing a new approach for the correct selection of planting varieties.

Comprehensive Study of Traditional Plant Ground Ivy (Glechoma hederacea L.) Grown in Croatia in Terms of Nutritional and Bioactive Composition

In the present study, ground ivy was harvested from different natural habitats in Croatia and subjected to screening analysis for nutritional and bioactive composition. To achieve maximum recovery of phenolic compounds, different extraction techniques were investigated-heat-assisted (HAE), microwave-assisted (MAE) and subcritical water (SWE) extraction. Prepared extracts were analysed by spectrophotometric methods, LC-MS/MS and HPLC-PAD methodologies. Results regarding nutritive analyses, conducted using standard AOAC methods, showed the abundance of samples in terms of insoluble dietary fibre, protein, calcium and potassium, while rutin, chlorogenic, cryptochlorogenic, caffeic and rosmarinic acid were the most dominant phenolic compounds. In addition, LC-MS/MS analysis revealed the presence of apigenin and luteolin in glycosylated form. Maximum recovery of target phenolic compounds was achieved with MAE, while SWE led to the formation of new antioxidants, which is commonly known as neoformation. Moreover, efficient prediction of phenolic composition of prepared extracts was achieved using NIR spectroscopy combined with ANN modelling.

Rapid quantification of dissolved solids and bioactives in dried root vegetable extracts using near infrared spectroscopy

Artificial neural networks (ANN) were developed for prediction of total dissolved solids, polyphenol content and antioxidant capacity of root vegetables (celery, fennel, carrot, yellow carrot, purple carrot and parsley) extracts prepared from the (i) fresh vegetables, (ii) vegetables dried conventionally at 50 °C and 70 °C, and (iii) the lyophilised vegetables. Two types of solvents were used: organic solvents (acetone mixtures and methanol mixtures) and water. Near-infrared (NIR) spectra were recorded for all samples. Principal Component Analysis (PCA) of the pre-treated spectra using Savitzky-Golay smoothing showed specific grouping of samples in two clusters (1st: extracts prepared using methanol mixtures and water as the solvents; 2nd: extracts prepared using acetone mixtures as the solvents) for all four types of extracts. Furthermore, obtained results showed that the developed ANN models can reliably be used for prediction of total dissolved solids, polyphenol content and antioxidant capacity of dried root vegetable extracts in relation to the recorded NIR spectra.

Influence of Sample Matrix on Determination of Histamine in Fish by Surface Enhanced Raman Spectroscopy Coupled with Chemometric Modelling

Histamine fish poisoning is a foodborne illness caused by the consumption of fish products with high histamine content. Although intoxication mechanisms and control strategies are well known, it remains by far the most common cause of seafood-related health problems. Since conventional methods for histamine testing are difficult to implement in high-throughput quality control laboratories, simple and rapid methods for histamine testing are needed to ensure the safety of seafood products in global trade. In this work, the previously developed SERS method for the determination of histamine was tested to determine the influence of matrix effect on the performance of the method and to investigate the ability of different chemometric tools to overcome matrix effect issues. Experiments were performed on bluefin tuna (Thunnus thynnus) and bonito (Sarda sarda) samples exposed to varying levels of microbial activity. Spectral analysis confirmed the significant effect of sample matrix, related to different fish species, as well as the extent of microbial activity on the predictive ability of PLSR models with R² of best model ranging from 0.722–0.945. Models obtained by ANN processing of factors derived by PCA from the raw spectra of the samples showed excellent prediction of histamine, regardless of fish species and extent of microbial activity (R² of validation > 0.99).