Insect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors

Colony identity clues for Syntermes grandis (Blattodea: Termitidae) individuals using near-infrared spectroscopy and PLS-DA approach

Termites are social insects with high species diversity in tropical ecosystems. Multivariate analysis with near-infrared spectroscopy (NIRS) and data interpretation can separate social insects belonging to different colonies of the same species. The objective of this study was to propose the use of discriminant analysis by partial least squares (PLS-DA) combined with NIRS to identify the colonial origin of the Syntermes grandis (Rambur, 1842) (Blattodea: Termitidae) in 2 castes. Six ground S. grandis colonies were identified and mapped; 30 workers and 30 soldier termites in each colony were submitted to spectral measurement with NIRS. PLS-DA applied to the termites' spectral absorbance was used to detect a spectral pattern per S. grandis colony by caste. PLS-DA regression with NIRS proved to be an approach with 99.9% accuracy for identifying the colonial origin of S. grandis workers and 98.3% for soldiers. The methodology showed the importance of qualitatively characterizing the colonial phenotypic response of this species. NIRS is a high-precision approach to identifying the colony origin of S. grandis workers and soldiers. The PLS-DA can be used to design ecological field studies to identify colony territorial competition and foraging behavior of subterranean termite species.

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.

Performance of benchtop and portable spectroscopy equipment for discriminating Iberian ham according to breed

Iberian ham is a highly appreciated product and according to Spanish legislation different labels identify different products depending on the genetic purity. Consequently, "100% Iberian" ham from purebred Iberian animals is more expensive than "Iberian" ham from Iberian x Duroc crosses. The hypothesis of this study was that to avoid labelling fraud it is possible to distinguish the breed (Iberian or Iberian x Duroc) of acorn-fed pigs of Iberian ham without any prior preparation of the sample by using spectroscopy that is a rapid and reliable technology. Moreover, portable devices which can be used in situ could provide similar results to those of benchtop equipment. Therefore, the spectra of the 60 samples (24 samples of 100% Iberian ham and 36 samples of Iberian x Duroc crossbreed ham) were recorded only for the fat, only for the muscle, or for the whole slice with two benchtop near-infrared (NIR) spectrometers (Büchi NIRFlex N-500 and Foss NIRSystem 5000) and five portable spectrometers including four portable NIR devices (VIAVI MicroNIR 1700 ES, TellSpec Enterprise Sensor, Thermo Fischer Scientific microPHAZIR, and Consumer Physics SCiO Sensor), and one RAMAN device (BRAVO handheld). The results showed that, in general, the whole slice recording produced the best results for classification purposes. The SCiO device showed the highest percentages of correctly classified samples (97% in calibration and 92% in validation) followed by TellSpec (100% and 81%). The SCiO sensor also showed the highest percentages of success when the analyses were performed only on lean meat (97% in calibration and 83% in validation) followed by microPHAZIR (84% and 81%), while in the case of the fat tissue. Raman technology showed the best discrimination capacity (96% and 78%) followed by microPHAZIR (89% and 81%). Therefore, spectroscopy has proved to be a suitable technology for discriminating ham samples according to breed purity; portable devices have been shown to give even better results than benchtop spectrometers.

Material Breakthroughs in Smart Food Monitoring: Intelligent Packaging and On-Site Testing Technologies for Spoilage and Contamination Detection

Despite extensive commercial and regulatory interventions, food spoilage and contamination continue to impose massive ramifications on human health and the global economy. Recognizing that such issues will be significantly eliminated by the accurate and timely monitoring of food quality markers, smart food sensors have garnered significant interest as platforms for both real-time, in-package food monitoring and on-site commercial testing. In both cases, the sensitivity, stability, and efficiency of the developed sensors are largely informed by underlying material design, driving focus toward the creation of advanced materials optimized for such applications. Herein, a comprehensive review of emerging intelligent materials and sensors developed in this space is provided, through the lens of three key food quality markers - biogenic amines, pH, and pathogenic microbes. Each sensing platform is presented with targeted consideration toward the contributions of the underlying metallic or polymeric substrate to the sensing mechanism and detection performance. Further, the real-world applicability of presented works is considered with respect to their capabilities, regulatory adherence, and commercial potential. Finally, a situational assessment of the current state of intelligent food monitoring technologies is provided, discussing material-centric strategies to address their existing limitations, regulatory concerns, and commercial considerations.

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.

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.

In silico NIR spectroscopy - A review. Molecular fingerprint, interpretation of calibration models, understanding of matrix effects and instrumental difference

Quantum mechanical calculations are routinely used as a major support in mid-infrared (MIR) and Raman spectroscopy. In contrast, practical limitations for long time formed a barrier to developing a similar synergy between near-infrared (NIR) spectroscopy and computational chemistry. Recent advances in theoretical methods suitable for calculation of NIR spectra opened the pathway to modeling NIR spectra of various molecules. Accurate theoretical reproduction of NIR spectra of molecules reaching the size of long-chain fatty acids was accomplished so far. In silico NIR spectroscopy, where the spectra are calculated ab initio, provides substantial improvement in our understanding of the overtones and combination bands that overlap in staggering numbers and create complex lineshape typical for NIR spectra. This improves the comprehension of the spectral information enabling access to rich and detail molecular footprint, essential for fundamental research and useful in routine analysis by NIR spectroscopy and chemometrics. This review article summarizes the most recent accomplishments in the emerging field with examples of simulated NIR spectra of molecules reaching long-chain fatty acids and polymers. In addition to detailed NIR band assignments and new physical insights, simulated spectra enable innovative support in applications. Understanding of the difference in the performance observed between miniaturized NIR spectrometers and chemical interpretation of the chemometric models are noteworthy here. These new elements integrated into NIR spectroscopy framework enable a knowledge-based design of the analysis with comprehension of the processed chemical information.

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

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 (R2c) and prediction (R2p) 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.

Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives

The ongoing miniaturization of spectrometers creates a perfect synergy with the common advantages of near-infrared (NIR) spectroscopy, which together provide particularly significant benefits in the field of food analysis. The combination of portability and direct onsite application with high throughput and a noninvasive way of analysis is a decisive advantage in the food industry, which features a diverse production and supply chain. A miniaturized NIR analytical framework is readily applicable to combat various food safety risks, where compromised quality may result from an accidental or intentional (i.e., food fraud) origin. In this review, the characteristics of miniaturized NIR sensors are discussed in comparison to benchtop laboratory spectrometers regarding their performance, applicability, and optimization of methodology. Miniaturized NIR spectrometers remarkably increase the flexibility of analysis; however, various factors affect the performance of these devices in different analytical scenarios. Currently, it is a focused research direction to perform systematic evaluation studies of the accuracy and reliability of various miniaturized spectrometers that are based on different technologies; e.g., Fourier transform (FT)-NIR, micro-optoelectro-mechanical system (MOEMS)-based Hadamard mask, or linear variable filter (LVF) coupled with an array detector, among others. Progressing technology has been accompanied by innovative data-analysis methods integrated into the package of a micro-NIR analytical framework to improve its accuracy, reliability, and applicability. Advanced calibration methods (e.g., artificial neural networks (ANN) and nonlinear regression) directly improve the performance of miniaturized instruments in challenging analyses, and balance the accuracy of these instruments toward laboratory spectrometers. The quantum-mechanical simulation of NIR spectra reveals the wavenumber regions where the best-correlated spectral information resides and unveils the interactions of the target analyte with the surrounding matrix, ultimately enhancing the information gathered from the NIR spectra. A data-fusion framework offers a combination of spectral information from sensors that operate in different wavelength regions and enables parallelization of spectral pretreatments. This set of methods enables the intelligent design of future NIR analyses using miniaturized instruments, which is critically important for samples with a complex matrix typical of food raw material and shelf products.

Detection of Cherry Quality Using YOLOV5 Model Based on Flood Filling Algorithm

Presently, the quality of cherries in the market is uneven, because human senses are used to distinguish cherry quality, which consumes a lot of time and energy and does not achieve good results in terms of accuracy. If the internal quality indices, such as the PH value, sugar–acid ratio, and vitamin C content, of cherries are extracted using chemical methods, the detection speed will decrease. With the development of artificial intelligence (AI), image processing by AI algorithms has attracted broad attention. The YOLOv5 model in the YOLO series has many advantages, such as high detection accuracy, fast speed, small size, and so on, and has been used in face recognition, image recognition and other fields. However, owing to the influence of seasonal weather, the environment and other factors, the dataset used in the training model decreases the accuracy of image recognition. To improve the accuracy, a large amount of data must be used for model training, but this will decrease the model training speed. Because it is impossible to use all data in training, there will inevitably be recognition errors in the detection process. In this study, the cherry images in a dataset were extracted by the flooding filling algorithm. The extracted cherry images were used as a new dataset for training and recognition, and the results were compared to those obtained with non-extracted images. The dataset generated by the flooding filling algorithm was used for model training. After 20 training epochs, the accuracy rate reached 99.6%. Without using the algorithm to extract images, the accuracy rate was only 78.6% after 300 training epochs.

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

Insects are gaining more and more space in food and feed sectors, creating an intense scientific interest towards insects as food ingredients. Several papers deal with cereal-based products complemented by insect powder in the past few years. However, adulteration and quality control of such products present some hot topics for researchers, e.g., how can we justify the amounts and/or species of the insects used in the given products? Our paper aims to answer such questions by analysing seven edible insect powders of different species independently. The mixtures with wheat flour were analysed using near infrared spectroscopy and chemometric methods. Not only powders of different species were clearly differentiated, but also mixtures created by different amounts of wheat flour. Prediction of insect content showed 0.65% cross-validated error. The proposed methodology gives an excellent tool for quality control of insect-based cereal food products.

Near-Infrared Spectroscopy and Mode Cloning (NIR-MC) for In-Situ Analysis of Crude Protein in Bamboo

This study develops Near-Infrared Spectroscopy (NIRS) and Mode-Cloning (MC) for the rapid assessment of the nutritional quality of bamboo leaves, the primary diet of giant pandas (Ailuropoda melanoleuca) and red pandas (Ailurus fulgens). To test the NIR-MC approach, we evaluated three species of bamboo (Phyllostachys bissetii, Phyllostachys rubromarginata, Phyllostachys aureosulcata). Mode-Cloning incorporated a Slope and Bias Correction (SBC) transform to crude protein prediction models built with NIR spectra taken from Fine–Ground leaves (master mode). The modified models were then applied to spectra from leaves in the satellite minimal processing modes (Course–Ground, Dry–Whole, and Fresh–Whole). The NIR-MC using the SBC yielded a residual prediction deviation (RPD) = 2.73 and 1.84 for Course–Ground and Dry–Whole sample modes, respectively, indicating a good quantitative prediction of crude protein for minimally processed samples that could be easily acquired under field conditions using a portable drier and grinder. The NIR-MC approach also improved the model of crude protein for spectra collected from Fresh–Whole bamboo leaves in the field. Thus, NIR-MC has the potential to provide a real-time prediction of the macronutrient distribution in bamboo in situ, which affects the foraging behavior and dispersion of giant and red pandas in their natural habitats.

Origin Identification of Hungarian Honey Using Melissopalynology, Physicochemical Analysis, and Near Infrared Spectroscopy

The objective of the study was to check the authenticity of Hungarian honey using physicochemical analysis, near infrared spectroscopy, and melissopalynology. In the study, 87 samples from different botanical origins such as acacia, bastard indigo, rape, sunflower, linden, honeydew, milkweed, and sweet chestnut were collected. The samples were analyzed by physicochemical methods (pH, electrical conductivity, and moisture), melissopalynology (300 pollen grains counted), and near infrared spectroscopy (NIRS:740–1700 nm). During the evaluation of the data PCA-LDA models were built for the classification of different botanical and geographical origins, using the methods separately, and in combination (low-level data fusion). PC number optimization and external validation were applied for all the models. Botanical origin classification models were >90% and >55% accurate in the case of the pollen and NIR methods. Improved results were obtained with the combination of the physicochemical, melissopalynology, and NIRS techniques, which provided >99% and >81% accuracy for botanical and geographical origin classification models, respectively. The combination of these methods could be a promising tool for origin identification of honey.

Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques

Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need to create Protected Geographical Indication (PGI) and Protected Designation of Origin (PDO) of crops and agricultural products to ensure the integrity of the food supply. In the present work, Visible and Near-Infrared Spectroscopy (Vis-NIR) combined with machine learning techniques was used to discriminate 16 millet varieties (n = 480) originating from various regions of China. Five different machine learning algorithms, namely, K-nearest neighbor (K-NN), Linear discriminant analysis (LDA), Logistic regression (LR), Random Forest (RF), and Support vector machine (SVM), were used to train the NIR spectra of these millet samples and to assess their discrimination performance. Visible cluster trends were obtained from the Principal Component Analysis (PCA) of the spectral data. Cross-validation was used to optimize the performance of the models. Overall, the F-Score values were as follows: SVM with 99.5%, accompanied by RF with 99.5%, LDA with 99.5%, K-NN with 99.1%, and LR with 98.8%. Both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying Vis-NIR spectroscopy assisted by machine learning technique can be an essential tool for tracing the origins of millet, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way.