Fish Meal Samples Research Articles

Microplastics contamination in commercial fish meal and feed: a major concern in the cultured organisms

The growing scale of plastic pollution causes a devastating impact on the aquatic ecosystem. The people largely depend on animal-based food for their protein requirements. In this study, we analysed 10 different fish meal samples and 20 feed samples used in farming to understand the level of microplastic (MPs) contamination and estimate the amount of MPs ingested by farmed fish, shrimp, and chicken through feed. The abundance of MPs in fish meal samples ranges from 210 ± 98.21 to 1154 ± 235.55 items/kg. The fish meal produced from dried fish is more prone to MPs contamination than that produced from fresh fish. In the case of fish feed, MP abundances range from 50 ± 22.36 to 160 ± 36.57 items/kg in shrimp feeds, 60 ± 26.74 to 230 ± 52.32 items/kg in fish feeds and 90 ± 25.11 to 330 ± 36.12 items/kg in chicken feeds. The exposure rate of MPs is higher in the grower- and finisher-stage feeds than in the starter feed. Fiber-shaped MPs of size 100–500 μm with PE and PP polymers were predominantly found in fish meal and feed samples. EDAX analysis showed the presence of Cr, Cd, Ti, Ni, Cu, As, Al, Pb, Hg, Cd, Ti, Fe, Ca, K, and Si in fish meal samples and Ca, Na, Zn, Cu, Ni, Cl, Al, Si, S, Pb, Cd, Ti, Cr, Mg and Fe in feed samples. The possible level of exposure of microplastic particles was calculated based on MP contamination in feed, feed consumption rate, and body weight. We estimated an MP exposure level of 531-1434 items/kg feed for farmed shrimp, 234–4480 items/kg feed for fishes, and 3519–434,280 items/kg feed for chicken. This study concludes that fish meal and feed are one of the important exposure routes of MPs to the farmed animals.

Evaluation of fish meal freshness using a metal-oxide semiconductor electronic nose combined with the long short-term memory feature extraction method.

To improve the classification and regression performance of the total volatile basic nitrogen (TVB-N) and acid value (AV) of different freshness fish meal samples detected by a metal-oxide semiconductor electronic nose (MOS e-nose), 402 original features, 62 manually extracted features, manually extracted and selected features by the RFRFE method, and the features extracted by the long short-term memory (LSTM) network were used as inputs to identify the freshness. The classification performance of the freshness grades and the estimation performance of the TVB-N and AV values of fish meal with different freshness were compared. According to the sensor response curve, preprocessing and feature extraction steps were first applied to the original data. Then, five classification algorithms and four regression algorithms were used for modeling. The results showed that a total of 30 features were extracted using the LSTM network, and the number of extracted features was significantly reduced. In the classification, the highest accuracy rate of 95.4% was obtained using the support vector machine method. In the regression, the least squares support vector regression method obtained the best root mean square error (RMSE). The coefficient of determination (R2), RMSE, and relative standard deviation (RSD) between the predicted value of TVBN and the actual value were 0.963, 11.01, and 7.9%, respectively. The R2, RMSE, and RSD between the predicted value of AV and the actual value were 0.972, 0.170, and 6.05%, respectively. The LSTM feature extraction method provided a new method and reference for feature extraction using an E-nose to identify other animal-derived material samples.

A novel “on-off-on” electrochemiluminescence aptasensor based on resonance energy transfer as signal amplification strategy for ultrasensitive detection of sulfadiazine in different samples

The misuse of antibiotics may contaminate the environment and cause harm to human health. Therefore, rapid and accurate detection of antibiotics is essential. In this study, a novel electrochemiluminescence resonance energy transfer (ECL-RET) pair was designed using a new ECL emitter (CPM, Ce-TBAPy) as the donor and Co-MOF@AuPt as the acceptor. Moreover, a highly sensitive and specific “on-off-on” ECL aptasensor was constructed for detecting sulfadiazine (SDZ). The aptasensor exhibited a broad linear range (from 10.0 fg mL−1 to 100 ng mL−1) for the SDZ concentration, with limit of detection and limit of quantification values of 1.14 fg mL−1and 3.75 fg mL−1, respectively. The aptasensor achieved good results in spiking experiments with milk and egg samples, and successfully quantified SDZ in fish meal quality control sample. The prepared aptasensor presents great potential for food and environmental safety by detecting antibiotics.

New approach for near-infrared wavelength selection using a combination of MIC and firefly evolution

Full-length spectral data analysis has a big problem that the variables are highly in collinearity and correlation. Spectral wavelength selection is a continuing hot topic in quantitative or qualitative analysis. In this paper, we propose a new approach for near-infrared (NIR) wavelength selection. The novel strategy mainly refers to the modification of maximum information coefficient (MIC) method and an improvement of firefly evolutionary algorithm. We introduce the orthogonal decomposition to modify the MIC method, so as to search the informative signals conceived in projection vectors. We also raise the common firefly algorithm (FA) as in the discretized mode, and design a novel adaptive mapping function to improve its intelligent computing effect. In experiment, the modified MIC (MICm) method and the adaptive discrete FA algorithm (DFAadp) are joint together for combined optimization of the NIR calibration model. The proposed combined modeling strategy is applied for quantitative analysis of the fishmeal samples, in the concern to select their informative variables/wavelengths. Experimental results indicate that the combination of MICm and DFAadp perform better than traditional MIC method and common DFA. We conclude that the proposed combined optimization strategy is beneficial for wavelength selection in NIR spectral analysis. It is anticipated to be validated for further applications in a wide range.

Evaluation of Dynamic Changes of Volatile Organic Components for Fishmeal during Storage by HS-SPME-GC-MS with PLS-DA.

Headspace solid-phase microextraction, combined with gas chromatography-mass spectrometry and partial least squares discriminant analysis, was adopted to study the rule of change in volatile organic compounds (VOCs) for domestic and imported fishmeal during storage with different freshness grades. The results showed that 318 kinds of VOCs were detected in domestic fishmeal, while 194 VOCs were detected in imported fishmeal. The total relative content of VOCs increased with storage time, among which acids and nitrogen-containing compounds increased significantly, esters and ketones increased slightly, and phenolic and ether compounds were detected only in domestic fishmeal. Regarding the volatile base nitrogen, acid value, pH value, and mold counts as freshness indexes, the freshness indexes were significantly correlated with nine kinds of VOCs (p < 0.05) through the correlation analysis. Among them, volatile base nitrogen had a significant correlation with VOCs containing nitrogen, acid value with VOCs containing carboxyl group and hydrocarbons, pH value with acids which could be used to adjust pH value, and mold counts with part of acids adjusting pH value and VOCs containing nitrogen. Due to the fact that the value of all freshness indexes increased with freshness degradation during storage, based on volatile base nitrogen and acid value, the fishmeal was divided into three freshness grades, superior freshness, corrupting, and completely corrupted. By using partial least squares discriminant analysis, this study revealed the differences in flavor of the domestic and imported fishmeal during storage with different freshness grades, and it identified four common characteristic VOCs, namely ethoxyquinoline, 6,7,8,9-tetrahydro-3H-benzo[e]indole-1,2-dione, hexadecanoic acid, and heptadecane, produced by the fishmeal samples during storage, as well as the characteristic VOCs of fishmeal at each freshness grade.

Per- and poly-fluoroalkyl substances in commercial organic eggs via fishmeal in feed

Chicken eggs can be a significant source of human PFAS exposure. A survey of PFAS in commercial eggs from larger farms across Denmark showed the absence or low contents of PFAS in free-range and barn eggs. However, organic eggs from eight farms collected in September 2022 had a similar profile of nine PFASs with a predominance of odd over even carbon length PFCAs. Farm 11–13 e.g. had egg yolk ng/g concentrations of PFOA 0.07 ± 0.02; PFNA 0.37 ± 0.04; PFDA 0.13 ± 0.00; PFUnDA 0.22 ± 0.04; PFDoDA 0.06 ± 0.02; PFTrDA 0.15 ± 0.04; PFTeDA 0.02 ± 0.02; PFHxS 0.10 ± 0.04; PFOS 2.62 ± 0.11. Normalised to PFOS, the relative sum of other PFAS showed no difference between the eight organic egg samples, but significant differences between mean individual PFASs (p = 1.4E-25), reflecting a similar profile. The PFAS found in two fishmeal samples with the same origin as the fishmeal used for the organic feed production, could account for the contents in the eggs via estimated transfer from the feed. Furthermore, the estimated transfer from concentration in feed to concentration in egg increased with the carbon length of the PFCA. Exposure (95th percentile) of ∑4PFAS (PFOA, PFNA, PFHxS, PFOS) solely from consumption of 311 g ∼ 5–6 organic eggs/week was for children 4–9 years 10.4 ng/kg bw, i.e. a significant exceedance of the tolerable weekly intake of 4.4 ng/kg bw established by the European Food Safety Authority. Based on the PFAS exposures from organic egg consumption, the organic egg producers decided voluntarily to cease adding fishmeal to the feed. Since the feed-to-egg half-lives are ≤1 week for PFOA, PFOS, and PFHxS, the removal of fishmeal as a feed ingredient should eliminate PFAS after 1–2 months. This was demonstrated in analyses of ten organic egg samples collected by the authorities without PFAS in eight and with 0.1 and 0.4 ng/g ∑4PFAS in two samples.

Effects of Gamma-Ray Irradiation of Bacteria Colonies in Animal Feeds and on Growth and Gut Health of Weaning Piglets

Animal feeds contain a substantial number and diversity of microorganisms, and some of them have pathogenic potential. The objectives of this study were to investigate the effects of different doses of gamma (γ)-ray irradiation on the bacteria count in different types of feed and then to test the effect of γ-ray-irradiation-treated fishmeal on the gut health and growth performance of weaning piglets. In trial 1, three fishmeal samples, two feather meal samples, three meat meal samples, three soybean meal samples, and three vitamin complexes were treated with γ-ray irradiation doses of 0, 3, 6, or 9 kGy. The 6 and 9 kGy doses eliminated most of the bacteria in the feed but also resulted in a loss of vitamin C and B1. In trial 2, 96 weaning piglets were fed one of the following three diets with eight replicates (pens) per group over a 14-day period: (1) the control diet—the basal diet supplemented with 6% fishmeal with a low bacteria count (40 CFU/g) and no E. coli; (2) the fishmeal-contaminated diet (FM-contaminated) diet—the basal diet supplemented with 6% fishmeal with a high bacteria count (91,500 CFU/g) and E. coli contamination; and (3) the irradiated fishmeal (irradiated FM) diet—the basal diet supplemented with γ-ray-irradiation-treated E. coli-contaminated fishmeal. The piglets that received the FM-contaminated diet had significantly lower average daily gain and a greater diarrhea index compared to those fed the control diet, whereas γ-ray irradiation treatment abrogated the negative effect of the E. coli-contaminated fishmeal. Collectively, γ-ray irradiation at a dose of 6–9 kGy was sufficient to eliminate the microorganisms in the feed, thereby benefitting the growth performance and gut health of the weaning piglets.

Development of a paper strip test for colorimetric detection of urea in raw materials for animal feed.

Aiming at developing an easily implementable method for on-site analysis to detect urea adulteration in feed ingredients, a simple and inexpensive paper strip for urea detection via colorimetric assay is herein presented. The paper strip can be simply fabricated by immobilizing urease with bromothymol blue (BTB) as a pH indicator on cellulose fiber. Upon dipping the paper strip into the target sample, the release of ammonia during the reaction between urea in the sample and urease on the paper strip causes a pH change that results in the development of a blue color, thus indicating the presence of urea. A semiquantitative detection method was developed on the basis of the color change on the paper strip, which can be detected by naked eyes and compared with a color chart made by spiking urea at concentrations varying from 0.10% to 1.0% (w/w) in animal protein and fishmeal samples. Moreover, quantitative data were obtained by taking a picture with a smartphone camera and measuring the color intensity using ImageJ software. A comparison between BTB and phenol red as pH indicators revealed that the former afforded better results in terms of resolution. Under optimal conditions, good linear responses of blue intensity were obtained in a concentration range of 0.10%-1.0% (w/w). The recovery was determined to range between 98.1% and 118.3% with a relative standard deviation of <5%. The developed paper strip assay was applied to determine urea in animal protein and fishmeal, finding good agreement with the official AOAC method (No. 967.07). The present paper strip is rapid and requires neither sophisticated devices nor skilled personnel, allowing its use by quality controllers for the routine on-site detection of the urea adulteration of raw materials.

Effectiveness of the Organic Acid-Based Antimicrobial Agent to Prevent Bacterial Contamination in Fish Meal.

Animal feed production is an important step of the food animal production chain in a farm-to-table model. The contamination of raw ingredients with foodborne pathogens in feed production remains as an important safety issue where pathogens may spread into food animals to cause illnesses in humans when affected food animals are consumed. In the present study, we aimed to examine the quality and microbial contamination of fish meal and to investigate the effectiveness of the organic acid-based antimicrobial agent SALTEC 514TM against Salmonella to prevent bacterial contamination in fish meal. Fish meal samples (n = 4) collected from feed mills at different locations were analyzed for protein and total volatile basic nitrogen (TVBN) content to assess their nutritional value and freshness, and its microbiological quality. The protein and TVBN content ranged from 53.2 ± 3.1 to 67.5 ± 2.3 g/100 g and 73.8 ± 4.5 to 100.4 ± 11.2 mg/100 g meal, respectively. Total plate count of the fish meal samples ranged from 2.0 ± 0.3 to 4.5 ± 0.5 log units, whereas suspected foodborne bacteria, Escherichia coli and Salmonella, were not detected in all samples. Fish meal samples were artificially contaminated (day 0) and re-challenged (day 30 and 90) with Salmonella Enteritidis (3 log CFU/g) to test for the effectiveness of SALTEC 514TM, an organic acid-based antimicrobial formulation, in preventing Salmonella contamination and recontamination during storage. SALTEC 514TM, when applied at three different doses, was found to reduce the number of Salmonella in monitored samples after one day of storage. A low dose of 0.5 kg/ton SALTEC 514TM prevented Salmonella recontamination from occurring in fish meal samples stored for 37 days. In medium (1.0 kg/ton) and high doses (3.0 kg/ton), applications of SALTEC 514TM prevented the Salmonella recontamination for a maximum storage duration of 97 days. The application of SALTEC 514TM in fish meal and/or other feed ingredients may prove to be a safe alternative to reduce the microbial load, especially of foodborne-related microorganisms, to contribute to feed and food safety.

The fishmeal adulteration identification based on microscopic image and deep learning

The fishmeal adulteration identification based on microscopic image and deep learning was studied in this paper, which included the qualitative identification and component recognition of adulterated fishmeal. Mobilenetv2 was chosen as the qualitative identification model to distinguish between fishmeal samples and adulterated samples. And the accuracy, precision, recall, and F1-measure of the test set were adopted as the evaluation indicators. The YOLOv3-Mobilenetv2 was selected as the component recognition model to distinguish components in adulterated samples, in which the feature pyramid structure and multi-scale feature fusion strategy were applied to solve the multi-scale problem in object detection. And the mean average precision (mAP) was adopted as the precision evaluation index and the frame per second (FPS) as the speed evaluation index of the component recognition model. At the same time, the effects of image enhancement preprocessing and resolution on the identification of fishmeal adulteration were also considered in this paper. It was shown that Mobilenetv2 was more suitable for qualitative identification when using the local constrained mask transformed microscopic images with a resolution of 224 × 224 pixels. The average accuracy, precision, recall, and F1-measure of the model were 93.02%, 93.11%, 93.25%, and 92.69% respectively. The improved YOLOv3-Mobilenetv2 was more suitable for component recognition with a resolution of 608 × 608 pixels. The mAP reached 78.49%, and the FPS was 45.97 f·s−1. The model of fishmeal adulteration identification adopted in this paper had high accuracy, which could quickly identify adulterated fishmeal and recognize components, and provide a new method and reference for improving the objectivity of fishmeal adulteration identification.

Nutritional Composition and Heavy Metal Contamination of Prominent Fishmeal Samples

Nutritional Composition and Heavy Metal Contamination of Prominent Fishmeal Samples

Microplastics in fish meals: An exposure route for aquaculture animals

Microplastics (MPs) are widely detected in many marine fishes. Fish meal contaminated by MP may constitute a potential threat to aquaculture animals. This study analyzed the characteristics of MP in fish meals from ten major fish meal-producing countries around the world. Microplastics were isolated from fish meal, examined under a microscope and identified using Fourier transform infrared microspectroscopy (μ-FT-IR). The results showed that MP pollution was widely detected in fish meal samples from ten countries. The average MP abundance of fish meals was 5.5 ± 1.6 items/g, with higher levels in China, Peru and Myanmar, which might be related to the high pollution level in fish and their habitats. In isolated MPs, fibers were the main shape type, and the most common size was 500–1000 μm. A total of 6 polymers were identified, with cellophane (CP), polypropylene (PP) and polyethylene teraphalate (PET) being the most common types. The total amount of MP ingestion from fish meals by different cultured animals was also estimated, with Atlantic salmon Salmon salar ingesting the largest number of MPs (9361 items), and red swamp crayfish Procambarus clarkii ingesting the smallest number of MPs (19 items). Thus, fish meal constitutes an important exposure route of MPs for aquaculture animals. The results of this study will provide a basis to assess the potential health risks of MPs in fish meals around the world.

PSVI-18 Nutritional Value of High-protein Yeast Products Fed to Growing Pigs in Comparison to Commonly Used Protein Sources in Swine Diets

Abstract Alternative ingredients and co-products from the ethanol and biofuel industries represent an opportunity for utilization in swine diets. The objective of this study was to determine standardized ileal digestibility (SID) of AA in two yeast-based high protein DDGS products (HP-DDGS) fed to growing pigs in comparison to common swine diet protein sources. Cornstarch-based diets were formulated using six ingredients as the sole source of AA: spray dried HP-GDDS, ring dried HP-GDDS, conventional DDGS, soybean meal (SBM), enzymatically-treated soybean meal (HP300) and fishmeal (FM). A N-free diet was used to estimate endogenous ileal AA losses. Seven barrows (25 ± 0.8 kg BW) were fitted with a simple T-cannula at the distal ileum. The experiment was conducted as a 7 × 7 Latin square design with 7 collection periods of 7d (5d adaptation and 2d ileal digesta collection). Pigs were fed at 4% of BW. Diets and ileal digesta were analyzed for AA and titanium. Digestibility values were calculated according to the marker method. The CP and AA concentrations in Spray HP-GDDS and Ring HP-GDDS samples were higher than in the DDGS sample and similar to the SBM and HP300 samples. General SID values were within the mean +/- SD of NRC (2012) values for the six ingredients evaluated. The SID of AA was greater (P &amp;lt; 0.05) in HP300 than in the other protein sources (90.09 vs. 79.97). The SID values were generally similar (P &amp;gt; 0.05) in SBM, FM, Spray HP-GDDS, and Ring HP-GDDS samples (81.49, 78.71, 81.52, and 79.20). The SID values for Lys were greater (P &amp;lt; 0.05) in Spray HP-GDDS than in Ring HP-GDDS and DDGS (83.56 vs. 77.33 and 68.53). The HP-GDDS products are potential alternative ingredients to commonly used protein sources in swine diets and the obtained data allows its use in practical diet formulation.

Quantitative analysis of fish meal freshness using an electronic nose combined with chemometric methods

Total volatile basic nitrogen (TVB-N) and acid value (AV) are the main indicators for characterizing fish meal freshness. To explore the feasibility of fish meal freshness detection based on an electronic nose system, a portable electronic nose measurement system was developed to detect the TVB-N and AV mass fraction in fish meal. The device is mainly composed of a gas acquisition and transmission module, a control and processing storage module with Raspberry Pi as the core, an ARPI600 data acquisition module, and a sensor array module. The system was used to collect the odour signals of fish meal samples with different TVB-N and AV mass fractions and the chemical test was conducted for the TVB-N and AV mass fractions in fish meal samples. The feature selection method of recursive feature elimination based on random forest (RFRFE) was used to select the sensor array features, and models of the TVB-N and AV mass fraction in fish meal were established by random forest regression (RFR) and least squares support vector machine (LSSVM) model based on the particle swarm optimization (PSO) algorithm, and the models were verified by a test sample. The experimental results show that the performance of the LSSVM regression model based on the RFRFE optimized sensor array data set is better than that of the traditional LSSVM model, The R2 and root mean square error (RMSE) between the predicted values and the real values for AV and TVB-N based on the test set are 0.8585, 0.3622 and 0.8789, 20.52, respectively. Therefore, the performance of feature selection using the RFRFE method is better. The results show that the TVB-N and AV of fish meal can be detected effectively by electronic nose technology.

Microplastics in fish and fishmeal: an emerging environmental challenge?

Microplastics are contaminants of emerging concern; they are ingested by marine biota. About a quarter of global marine fish landings is used to produce fishmeal for animal and aquaculture feed. To provide a knowledge foundation for this matrix we reviewed the existing literature for studies of microplastics in fishmeal-relevant species. 55% of studies were deemed unsuitable due to focus on large microplastics (> 1 mm), lack of, or limited contamination control and polymer testing techniques. Overall, fishmeal-relevant species exhibit 0.72 microplastics/individual, with studies generally only assessing digestive organs. We validated a density separation method for effectiveness of microplastic extraction from this medium and assessed two commercial products for microplastics. Recovery rates of a range of dosed microplastics from whitefish fishmeal samples were 71.3 ± 1.2%. Commercial samples contained 123.9 ± 16.5 microplastics per kg of fishmeal—mainly polyethylene—including 52.0 ± 14.0 microfibres—mainly rayon. Concentrations in processed fishmeal seem higher than in captured fish, suggesting potential augmentation during the production process. Based on conservative estimates, over 300 million microplastic particles (mostly < 1 mm) could be released annually to the oceans through marine aquaculture alone. Fishmeal is both a source of microplastics to the environment, and directly exposes organisms for human consumption to these particles.

ВЕТЕРИНАРНО-САНИТАРНАЯ ОЦЕНКА КОРМОВ ДЛЯ ВЫРАЩИВАНИЯ ОСЕТРОВЫХ РЫБ

The purpose of the work is a veterinary and sanitary examination of the quality and safety of feed for sturgeon fish, including the analysis of changes in the microflora of feed during storage in terms of organoleptic, physicochemical and microbiological indicators. Samples of fish meal, reworked loose fish meal, readymade fish food were taken for the study. It has been established that 6 % of feed does not meet the regulatory requirements for organoleptic indicators, 3 % – for physicochemical indicators in excess of the mass fraction of crude ash by 57 % and the content of mass fraction of crude fat by 10 %. The content of MRL of heavy metals and toxicological indicators correspond to the regulatory requirements for all types of studied feed. Microbiological indicators did not meet the regulatory requirements in 6 % of samples for the presence of sulfide-reducing anaerobes, microscopic molds in one sample of fish meal and the content of Alternaria ssp, Penicillium patulum, Aspergillus ssp and yeast Zygosaccaromyces ssp and Xeromyces ssp in one sample of ready-made fish. At storing until the end of the shelf life, 94 % samples of fish meal from two manufacturers were benign in all respects, they could withstand storage for 6 months without signs of spoilage, 90 % of the samples of the finished fish food microbiological stability until the end of the shelf life and another 14 days after the expiration date declared by the manufacturer. At all stages of the study pathogenic bacteria: Salmonella ssp., enter pathogenic E. coli, L. monocytogenes, S aureus and other coagulase-positive staphylococci were absent in all studied feeds. The species composition of fishmeal microflora changed during storage, the amount of mesophilic microflora decreased, the microflora of the feed was represented by saprophytic spore-forming microorganisms of the genera Bacillus ssp, including Bac. сereus 0,5×105 UFC/G and Clostridium ssp, psychrophilic bacteria of the genera Pseudomonas and Enterococcus. The greatest accumulation of microflora was observed on the 35th day of storage.

A hybrid optimization method for sample partitioning in near-infrared analysis

The division of calibration and validation is one of the essential procedures that affect the prediction result of the calibration model in quantitative analysis of near-infrared (NIR) spectroscopy. The conventional methods are Kennard-Stone (KS) and sample set partitioning based on joint x-y distances (SPXY). These algorithms use Euclidean distance to cover as many representative samples as possible. This paper proposes an Adaptive Hybrid Cuckoo-Tabu Search (AHCTS) algorithm for partitioning samples based on optimization. The algorithm combines the characteristics of cuckoo search (CS) and tabu search (TS) and fuses with an adaptive function. For comparison, using fishmeal samples as spectral analysis data, KS, SPXY, and AHCTS algorithms were used to divide the modeling samples to establish partial least squares regression (PLSR) models. The experimental results showed that the model established by the proposed algorithm performs better than KS and SPXY. It reveals that the AHCTS method may be an advantageous alternative for quantitative analysis of NIR spectroscopy.

A Novel Genetic Algorithm-Based Optimization Framework for the Improvement of Near-Infrared Quantitative Calibration Models.

The global fishmeal production is used for animal feed, and protein is the main component that provides nutrition to animals. In order to monitor and control the nutrition supply to animal husbandry, near-infrared (NIR) technology was utilized for rapid detection of protein contents in fishmeal samples. The aim of the NIR quantitative calibration is to enhance the model prediction ability, where the study of chemometric algorithms is inevitably on demand. In this work, a novel optimization framework of GSMW-LPC-GA was constructed for NIR calibration. In the framework, some informative NIR wavebands were selected by grid search moving window (GSMW) strategy, and then the variables/wavelengths in the waveband were transformed to latent principal components (LPCs) as the inputs for genetic algorithm (GA) optimization. GA operates in iterations as implementation for the secondary optimization of NIR wavebands. In steps of the variable's population evolution, the parametric scaling mode was investigated for the optimal determination of the crossover probability and the mutation operator. With the GSMW-LPC-GA framework, the NIR prediction effect on fishmeal protein was experimentally better than the effect by simply adopting the moving window calibration model. The results demonstrate that the proposed framework is suitable for NIR quantitative determination of fishmeal protein. GA was eventually regarded as an implementable method providing an efficient strategy for improving the performance of NIR calibration models. The framework is expected to provide an efficient strategy for analyzing some unknown changes and influence of various fertilizers.

Fish meal freshness detection by GBDT based on a portable electronic nose system and HS-SPME–GC–MS

The volatile compounds of super fresh, superior fresh, general fresh, corrupt and completely corrupt fish meal were studied by headspace solid-phase microextraction–gas chromatography–mass spectrometry. Principal component analysis was used to analyze the sensor and gas chromatography-mass spectrometry data, so as to study the resolution of sensor array. A total of 198 fish meal samples with different freshness were classified by the gradient boosting decision tree method, yielding a model between the sensor array data and the freshness index, such as the acid value and the volatile base nitrogen value. The gradient boosting decision tree model shows that the correlation between the predicted acid value by electronic nose and the measured acid value is 0.90 and the correlation between the predicted volatile base nitrogen value and the measured volatile base nitrogen is 0.97. The combination of an electronic nose and a pattern recognition method based on a gas sensor can predict the acid value and volatile base nitrogen of the freshness index and can detect the freshness of fish meal.

Can Near Infrared Spectroscopy (NIRS) Quantify The Quality of Fishmeal Circulating in Jember, Indonesia?

The objectives of this study were to determine the range of nutrient content of fishmeal circulating in Jember Regency, East Java, and to make a calibration standard for quick calculation using NIRS by R2 and RMSECV. This study used 11 fishmeal samples from around Puger, Jember and around Muncar, Banyuwangi. The physical examination included organoleptic tests (color, odor, and texture), density, and chemical analysis of fishmeal samples. The data obtained were analyzed descriptively. The results indicated that the nutrient content of fishmeal had greatly variation. The CP content presented a quite high ranged between 26.61% to 64.30%. The result of organoleptic test showed the color was yellowish brown, brown, slightly brown to dark brown. The odor was generally standard, which was fishy. Some fishmeal samples were moist in texture, such as fishmeal samples from Jember A (22.03%) and Muncar Banyuwangi B (19.65%). The average of fishmeal density was 0.528 g/ml that ranged between 0.341-0.726 g/ml. The measurement of R2 and RMSECV calibration of fishmeal using NIRS showed good results to predict CF, DM, and EE contents. The usage of NIRS technology was recommended for proximate analysis unless ash content due to the absence of spectrum absorption for minerals.