Analysis of wheat flour-insect powder mixtures based on their near infrared spectra

Near-infrared spectroscopy (NIRS) for monitoring the nutritional composition of black soldier fly larvae (BSFL) and frass

BACKGROUND

The demand for protein obtained from animal sources is growing rapidly, as is the necessity for sustainable animal feeds. The use of black soldier fly larvae (BSFL) reared on organic side streams as sustainable animal feed has been receiving attention lately. This study assessed the ability of near-infrared spectroscopy (NIRS) combined with chemometrics to evaluate the nutritional profile of BSFL instars (fifth and sixth) and frass obtained from two different diets, namely soy waste and customised bread-vegetable diet. Partial least squares (PLS) regression with leave one out cross-validation was used to develop models between the NIR spectral data and the reference analytical methods.

RESULTS

Calibration models with good [coefficient of determination in calibration (Rcal 2 ): 0.90; ratio of performance to deviation (RPD) value: 3.6] and moderate (Rcal 2 : 0.76; RPD value: 2.1) prediction accuracy was observed for acid detergent fibre (ADF) and total carbon (TC), respectively. However, calibration models with moderate accuracy were observed for the prediction of crude protein (CP) (Rcal 2 : 0.63; RPD value: 1.4), crude fat (CF) (Rcal 2 : 0.70; RPD value: 1.6), neutral detergent fibre (NDF) (Rcal 2 : 0.60; RPD value: 1.6), starch (Rcal 2 : 0.52; RPD value: 1.4), and sugars (Rcal 2 : 0.52; RPD value: 1.4) owing to the narrow or uneven distribution of data over the range evaluated.

CONCLUSION

The near-infrared (NIR) calibration models showed a good to moderate prediction accuracy for the prediction of ADF and TC content for two different BSFL instars and frass reared on two different diets. However, calibration models developed for predicting CP, CF, starch, sugars and NDF resulted in models with limited prediction accuracy. © 2023 Society of Chemical Industry.

Evaluating the Use of Vibrational Spectroscopy to Detect the Level of Adulteration of Cricket Powder in Plant Flours: The Effect of the Matrix

Edible insects have been recognised as an alternative food or feed ingredient due to their protein value for both humans and domestic animals. The objective of this study was to evaluate the ability of both near- (NIR) and mid-infrared (MIR) spectroscopy to identify and quantify the level of adulteration of cricket powder added into two plant proteins: chickpea and flaxseed meal flour. Cricket flour (CKF) was added to either commercial chickpea (CPF) or flaxseed meal flour (FxMF) at different ratios of 95:5% w/w, 90:10% w/w, 85:15% w/w, 80:20% w/w, 75:25% w/w, 70:30% w/w, 65:35% w/w, 60:40% w/w, or 50:50% w/w. The mixture samples were analysed using an attenuated total reflectance (ATR) MIR instrument and a Fourier transform (FT) NIR instrument. The partial least squares (PLS) cross-validation statistics based on the MIR spectra showed that the coefficient of determination (R2CV) and the standard error in cross-validation (SECV) were 0.94 and 6.68%, 0.91 and 8.04%, and 0.92 and 4.33% for the ALL, CPF vs. CKF, and FxMF vs. CKF mixtures, respectively. The results based on NIR showed that the cross-validation statistics R2CV and SECV were 0.95 and 3.16%, 0.98 and 1.74%, and 0.94 and 3.27% using all the samples analyzed together (ALL), the CPF vs. CKF mixture, and the FxMF vs. CKF mixture, respectively. The results of this study showed the effect of the matrix (type of flour) on the PLS-DA data in both the classification results and the PLS loadings used by the models. The different combination of flours (mixtures) showed differences in the absorbance values at specific wavenumbers in the NIR range that can be used to classify the presence of CKF. Research in this field is valuable in advancing the application of vibrational spectroscopy as routine tools in food analysis and quality control.

Proximate Content Monitoring of Black Soldier Fly Larval (Hermetia illucens) Dry Matter for Feed Material using Short-Wave Infrared Hyperspectral Imaging

Edible insects are gaining popularity as a potential future food source because of their high protein content and efficient use of space. Black soldier fly larvae (BSFL) are noteworthy because they can be used as feed for various animals including reptiles, dogs, fish, chickens, and pigs. However, if the edible insect industry is to advance, we should use automation to reduce labor and increase production. Consequently, there is a growing demand for sensing technologies that can automate the evaluation of insect quality. This study used short-wave infrared (SWIR) hyperspectral imaging to predict the proximate composition of dried BSFL, including moisture, crude protein, crude fat, crude fiber, and crude ash content. The larvae were dried at various temperatures and times, and images were captured using an SWIR camera. A partial least-squares regression (PLSR) model was developed to predict the proximate content. The SWIR-based hyperspectral camera accurately predicted the proximate composition of BSFL from the best preprocessing model; moisture, crude protein, crude fat, crude fiber, and crude ash content were predicted with high accuracy, with R2 values of 0.89 or more, and root mean square error of prediction values were within 2%. Among preprocessing methods, mean normalization and max normalization methods were effective in proximate prediction models. Therefore, SWIR-based hyperspectral cameras can be used to create automated quality management systems for BSFL.

Near Infrared Spectroscopy for Prediction of Yeast and Mould Counts in Black Soldier Fly Larvae, Feed and Frass: A Proof of Concept

The use of black soldier fly larvae (BSFL) grown on different organic waste streams as a source of feed ingredient is becoming very popular in several regions across the globe. However, information about the easy-to-use methods to monitor the safety of BSFL is a major step limiting the commercialization of this source of protein. This study investigated the ability of near infrared (NIR) spectroscopy combined with chemometrics to predict yeast and mould counts (YMC) in the feed, larvae, and the residual frass. Partial least squares (PLS) regression was employed to predict the YMC in the feed, frass, and BSFL samples analyzed using NIR spectroscopy. The coefficient of determination in cross validation (R2CV) and the standard error in cross validation (SECV) obtained for the prediction of YMC for feed were (R2cv: 0.98 and SECV: 0.20), frass (R2cv: 0.81 and SECV: 0.90), larvae (R2cv: 0.91 and SECV: 0.27), and the combined set (R2cv: 0.74 and SECV: 0.82). However, the standard error of prediction (SEP) was considered moderate (range from 0.45 to 1.03). This study suggested that NIR spectroscopy could be utilized in commercial BSFL production facilities to monitor YMC in the feed and assist in the selection of suitable processing methods and control systems for either feed or larvae quality control.

Insects as food - Changes in consumers' acceptance of entomophagy in Hungary between 2016 and 2021

Public interest in entomophagy (consumption of insects) has developed significantly over the past several years. Possible nutritional benefits are perceived by consumers according to several recent studies, as well as sustainability and food security. However, most European communities, including the Hungarian, do not embrace entomophagy, despite the widespread practice elsewhere globally. This study aims to evaluate the changes in the perception of entomophagy among the Hungarian population between 2016 and 2021, together with the factors differentiating between acceptive and dismissive consumers. The results of the two representative quantitative surveys indicate that more than 70% of Hungarian consumers are not willing to try entomophagy, which had not changed significantly in the observed period, despite the high media coverage of this topic in recent years. Some groups open to insect consumption can still be identified. According to the socioeconomic segmentation of the data collected in 2021, consumers who accept insect-based foods can be found in high numbers among men between 18 and 39 years old (49.3%). Positive attitudes are less likely to be observed among females; however, 27.6% of highly educated women between 18 and 59 years demonstrated a certain level of interest. Those consumers willing to consume insects are driven mainly by curiosity, and also value high protein content and sustainability, and perceive insect-based food as nutritious. Consumers who prefer local and national food tend to refuse to eat insects in a higher ratio.

Analysis of the Composition of Different Instars of Tenebrio molitor Larvae using Near-Infrared Reflectance Spectroscopy for Prediction of Amino and Fatty Acid Content

Insects are a sustainable protein source for food and feed. The yellow mealworm (Tenebrio molitor L.) is a promising candidate for industrial insect rearing and was the focus of this study. This research revealed the diversity of Tenebrio molitor larvae in the varying larval instars in terms of the nutritional content. We hypothesized that water and protein are highest in the earlier instar, while fat content is very low but increases with larval development. Consequently, an earlier instar would be a good choice for harvest, since proteins and amino acids content decrease with larval development. Near-infrared reflectance spectroscopy (NIRS) was represented in this research as a tool for predicting the amino and fatty acid composition of mealworm larvae. Samples were scanned with a near-infrared spectrometer using wavelengths from 1100 to 2100 nm. The calibration for the prediction was developed with modified partial least squares (PLS) as the regression method. The coefficient for determining calibration (R²_C) and prediction (R²_P) were >0.82 and >0.86, with RPD values of >2.20 for 10 amino acids, resulting in a high prediction accuracy. The PLS models for glutamic acid, leucine, lysine and valine have to be improved. The prediction of six fatty acids was also possible with the coefficient of the determination of calibration (R²_C) and prediction (R²_P) > 0.77 and >0.66 with RPD values > 1.73. Only the prediction accuracy of palmitic acid was very weak, which was probably due to the narrow variation range. NIRS could help insect producers to analyze the nutritional composition of Tenebrio molitor larvae fast and easily in order to improve the larval feeding and composition for industrial mass rearing.

Near-Infrared Reflectance Spectroscopy for Quantitative Analysis of Fat and Fatty Acid Content in Living Tenebrio molitor Larvae to Detect the Influence of Substrate on Larval Composition

Several studies have shown that mealworms (Tenebrio molitor L.) could provide animals and humans with valuable nutrients. Tenebrio molitor larvae were studied to determine whether their rearing diets affected their fat and fatty acid content and to ascertain if it is possible to detect the changes in the larval fat composition using near-infrared reflectance spectroscopy (NIRS). For this reason, a standard control diet (100% wheat bran) and an experimental diet, consisting of wheat bran and the supplementation of a different substrate (coconut flour, flaxseed flour, pea protein flour, rose hip hulls, grape pomace, or hemp protein flour) were used. The results showed lesser weight gain and slower growth rates for larvae raised on diets with a high fat content. A total of eight fatty acids were identified and quantified, where palmitic, oleic, and linoleic acids were the most prevalent and showed a correlation between larval content and their content in the rearing diets. There was a high content of lauric acid (3.2-4.6%), myristic acid (11.4-12.9%), and α-linolenic acid 8.4-13.0%) in mealworm larvae as a result of the high dietary content of these fatty acids. NIR spectra were also influenced by the fat and fatty acid composition, as larval absorbance values differed greatly. The coefficient of the determination of prediction (R²_P) was over 0.97, with an RPD value of 8.3 for the fat content, which indicates the high predictive accuracy of the NIR model. Furthermore, it was possible to develop calibration models with great predictive efficiency (R²_P = 0.81-0.95, RPD = 2.6-5.6) for all fatty acids, except palmitoleic and stearic acids which had a low predictive power (R²_P < 0.5, RPD < 2.0). The detection of fat and fatty acids using NIRS can help insect producers to quickly and easily analyze the nutritional composition of mealworm larvae during the rearing process.

Exploring the Analytical Complexities in Insect Powder Analysis Using Miniaturized NIR Spectroscopy

Insects have been a food source for humans for millennia, and they are actively consumed in various parts of the world. This paper aims to ascertain the feasibility of portable near-infrared (NIR) spectroscopy as a reliable and fast candidate for the classification of insect powder samples and the prediction of their major components. Commercially-available insect powder samples were analyzed using two miniaturized NIR instruments. The samples were analyzed as they are and after grinding, to study the effect of the granulometry on the spectroscopic analyses. A homemade sample holder was designed and optimized for making reliable spectroscopic measurements. Classification was then performed using three classification strategies, and partial least squares (PLS) regression was used to predict the macronutrients. The results obtained confirmed that both spectroscopic sensors were able to classify insect powder samples and predict macronutrients with an adequate detection limit.

Determination of Moisture and Protein Content in Living Mealworm Larvae (Tenebrio molitor L.) Using Near-Infrared Reflectance Spectroscopy (NIRS)

Simple Summary

Insects are increasingly becoming a new protein source for animal feed and human food. Spectroscopic methods, such as near-infrared reflectance spectroscopy, represent a non-destructive and rapid technique that can be applied to perform an online analysis in chemical composition. The aim of the research was to determine the moisture and protein content of living mealworm larvae using near-infrared spectroscopy as a new technique in analyzing nutritional changes. The prediction results of the near-infrared reflectance measurements of living mealworm larvae are presented in this study. The moisture and protein content of the larvae could be predicted with high accuracy and were specifically manipulated by using different water sources (pure water and carrots) and amounts and varying humidity. It was also determined that the larvae can be optimally provided with pure water as well as carrots. High humidity led to faster growth and a higher final weight, which has a positive effect on reducing the time to harvest. This study can help insect producers to have the possibility to measure the composition of the larvae quickly and easily using near-infrared spectroscopy, modify larval composition with regard to water and protein content and improve rearing conditions in terms of water supply for mealworm larvae.

Abstract

Yellow mealworm larvae (Tenebrio molitor L.) are a sustainable source of protein for food and feed. This study represents a new approach in analyzing changes in the nutritional composition of mealworm larvae using near-infrared reflectance spectroscopy (NIRS) combined with multivariate analysis. The moisture and protein content of living larvae were scanned with a near-infrared spectrometer using wavelengths from 1100 to 2100 nm. Different feeding groups with varying moisture sources and amount and the difference between low (50%) and high (75%) humidity were tested, and the influence on larval moisture and protein content was measured. A calibration was developed, with modified partial least squares as the regression method. The NIR spectra were influenced by the moisture and protein content of the larvae, because the absorbance values of the larval groups differed greatly. The coefficient of the determination of calibration (R²_c) and prediction (R²_p) were over 0.98 for moisture and over 0.94 for protein content. The moisture source and content also had a significant influence on the weight gain of the larvae. Consequently, significant differences in protein content could be determined, depending on the water supply available. With respect to wet weight, the larvae moisture content varied from 60 to 74% and protein content from 16 to 24%. This investigation revealed that with non-invasive NIRS online monitoring, the composition of insects can be continuously recorded and evaluated so that specific feeding can be carried out in the course of larval development and composition.