Preliminary Study on the Determination of ppm-Level Concentration of Histamine in Tuna Fish Using a Dry Extract System for Infrared Coupled with Near-Infrared Spectroscopy

Primary assessment of macronutrients in durian (CV Monthong) leaves using near infrared spectroscopy with wavelength selection

Farmers would be able to regulate fertilization and produce quality durian if they knew the nutrient concentration in durian leaves. A long period of time for traditional nutritional content determination is needed. Therefore, near-infrared spectroscopy is a good method for nondestructive and quick nutrient content evaluation. The leaf sample matrices (fresh leaves, dried ground leaves, and dried ground leaf pellets) were scanned by Fourier transform near-infrared (FT-NIR) with a wavelength of 12,500-3,600 cm-1. Regression models were developed using partial least squares (PLS) with full wavelength, short wavelength, and selected wavelength by successive projections algorithm (SPA). In this study, the model for N and K concentration was acceptable and the prediction was considered good but for P content not had succeeded. As a result, the PLS-SPA model using fresh leaf samples for evaluating N content in durian leaves exhibited performance of r2 = 0.852, SEP = 0.14%, RPD = 2.63 and bias = -0.020%. The PLS-SPA model using dried ground leaf samples for evaluating K content in durian leaves exhibited performance of r2 = 0.820, SEP = 0.13%, RPD = 2.36 and bias = 0.006%. This research found that it is possible to apply NIR waves to predict N and K concentrations in durian leaves. It is not necessary to predict directly from the wavelengths associated with -N or -K bonds. Instead, NIR can measure them indirectly from the bonding of proteins, which are products formed by N and K. In addition, selecting the wavelength that is related to the value to be measured can produce results that are not significantly different from using full or short wavelengths. These models can assist farmers in rapidly predicting N and K content in durian leaves for immediate fertilizer adjustment.

Determination of histamine levels in fresh fish using Near Infrared (NIR) technology

Fresh fish and fish products are highly perishable food. Development of fast, secure and non- destructive technique for estimation the presence and quantity of components which are related with safety of food products is of great interest for science but also and for the industry sector. Above-mentioned idea is the aim of this research work, precisely, determination of histamine levels in fresh fish with the use of different near infrared handheld devices. Histamine is one of biogenic amines among putrescine, cadaverine, tyramine which are non-volatile amines produced post mortem and are formed from decarboxylation of specific free amino acids in fish or shellfish tissue. Reason why we choose to developing technique for evaluation of histamine levels is EU Regulation 2073/2005 requires the determination of histamine at three different levels: 100, 200 and 400 mg/kg. For reaching the goal we decided to use paper-based technology for collecting samples for evaluation using Whatman grade 2 qualitative filter papers. In the first phase for validation of NIR devices specifications, and for creation of calibration curve we used histamine solution with 0.5, 1, 2.5 and 5 %. Results showed regression of R2 = 0.69 which indicates that NIR devices can be promising tools for spotting histamine beside small molecular weight.

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).

Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg⁻¹ using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r²) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg⁻¹ and root mean square of cross-validation (RMSECV) ≤ 6 mg kg⁻¹ were achieved. Both models were optimal also in the validation stage, showing r² values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg⁻¹ and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

Proximal Composition and Nutritive Value of Raw, Smoked and Pickled Freshwater Fish

The aim of the study was to assess protein, fat, salt, collagen, moisture content and energy value of freshwater fish purchased in Polish fish farms. Eight species of freshwater fish (raw, smoked, pickled) were assessed by near infrared spectroscopy (NIRS). The protein content varied between 15.9 and 21.7 g/100 g, 12.8 and 26.2 g/100 g, 11.5 and 21.9 g/100 g in raw, smoked and pickled fish, respectively. Fat content ranged from 0.89 to 22.3 g/100 g, 0.72 to 43.1 g/100 g, 0.01 to 29.7 g/100 g in raw, smoked and pickled fish, respectively. Salt content ranged from 0.73 to 1.48 g/100 g, 0.77 to 3.39 g/100 g, 1.47 to 2.29 g/100 g in raw, smoked and pickled fish, respectively. A serving (150 g) of each fish product provided 53.2–71.9% of the Reference Intake (RI) for protein, 2.21–60.3% of the RI for fat, 21.3–61.3% of the RI for salt and 6.27–24.4% kJ/6.29–24.5% kcal of the RI for energy. Smoked fish had a higher protein and also fat content than raw and pickled fish, while smoked and pickled fish had higher salt content than raw fish. Cluster analysis was performed, which allowed to distinguish, on the basis of protein, fat, salt, collagen and moisture content, mainly European eel.

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.