The consumption and farming of insects are gaining global attention as sustainable alternatives to conventional protein sources. Industrial processing of insects into powders or pastes complicates species identification, raising concerns about product authenticity, food safety, and potential fraud. In Western countries, particularly in Europe, the sector is expanding under a stringent regulatory framework, especially regarding rearing substrates, which hinders economic development. This study aimed to assess the species authenticity of commercial insect-based food and feed products and detect the presence of animal-derived DNA from unauthorized substrates. A total of 119 samples (pure insect meals and processed products) were collected from various origins. Species-specific real-time PCR assays targeted Tenebrio molitor, Hermetia illucens, Alphitobius diaperinus, Acheta domesticus, Bombyx mori, and Gryllodes sigillatus, alongside assays for ruminant, porcine, and poultry DNA. High-throughput sequencing (HTS) using metabarcoding confirmed and broadened species detection. Most samples contained the declared species; however, cases of mislabeling, substitution, and cross-contamination were observed. A few insect meals contained animal DNA which could suggest potential use of prohibited substrates. These findings highlight the urgent need for standardized authentication methods and improved transparency to ensure regulatory compliance, consumer trust, and sustainable development of the insect-based sector.

Since their approval for use in aquaculture in 2017, processed insect proteins have been extensively studied for their nutritional quality in animal feed. This new type of meal is highly promising but requires, as for other products used in animal feed, strict sanitary control in accordance with European legislation. Within this legal framework, light microscopy and PCR remain the official methods but have some analytical limitations that other methods could overcome. This paper aims to provide an overview of the European legislation concerning use of processed insect proteins, but also to highlight the advantages and disadvantages of the official methods for their analysis. It also points out other analytical methods, which have already proved their worth for the analysis of processed animal proteins, which could be used as complementary methods.

The silkworm, Bombyx mori, is reared on a large scale, mainly for silk production. The waste from this silk production, like pupae, is underused. As an edible insect, B. mori is a good source of protein in human food and animal feed. In recent years, European legislation on the use of insects has evolved and a multitude of European companies have initiated the rearing of insects specifically for food and feed applications. Regarding animal feed, Commission Regulations (EU) 2021/1372 and 2021/1925 authorize eight insect species, including silkworm, as processed animal proteins for use in fish, pig, and poultry feed. The incorporation of edible insects into the human diet falls within Regulation (EU) No. 2015/2283 concerning novel foods. Implementation of authentication methods is imperative to ensure the conformity of the products. In the present study, we propose a specific real-time PCR method for the detection of silkworm (B. mori). The developed PCR test amplifies a 98 bp fragment of the cadherin gene. This gene is present in a single-copy per haploid genome, as demonstrated by experimental evidence. The qualitative method was successfully evaluated on the performance criteria of specificity, sensitivity, efficiency, robustness, and transferability. The applicability of the test was assessed on samples of B. mori from industry. Light microscopy and DNA metabarcoding approaches were used as a complement to genomic analysis as a means of providing authentication of the samples.

Since 2017, insect meals have been authorised for animal feed in the European Union, but only eight insect species can be used to produce these meals. This legislation brings with it new analytical challenges, such as the need to identify the insect species processed in the meal. This paper investigated the ability of Near Infrared Microscopy (NIRM) to discriminate meal particles from authorised insect species and differentiate them from unauthorised species. The spectral data was analysed using chemometrics, enabling the assessment of classification specificity and sensitivity for each species to be discriminated. Using Partial Least Squares Discriminant Analysis (PLS-DA), insect species could be efficiently discriminated with specificity and sensitivity values generally above 0.90. However, the discrimination between particular species appeared more difficult. Most of the observed confusion is probably due to the chemical composition of the insects, which can be very similar between closely related species. These results were encouraging, but also indicated that the use of the NIRM technique alone in case of fraud or natural contamination should be complemented by other techniques such as RT-PCR or mass spectrometry.

The introduction of terrestrial invertebrate processed animal proteins (PAPs) from insects in animal feed has introduced challenges for official controls in the European Union. Currently, their detection relies only on light microscopy and is entirely dependent on the expertise of trained operators. Recent studies have highlighted two primary problems regarding insect PAP detection: specificity issues due to the misidentification of non-insect structures as insect derived ones, and the difficulty in categorizing a wide morphological diversity of particles resulting from the grinding of whole insect larvae. To address these limitations, a proof of concept was developed involving automated microscopic image analysis and classification by deep learning. A classification pipeline integrating a YOLO object detection model with an improved ConvNeXt architecture coupled with transformer blocks was tested. After supervised learning, training and validation of the model, obtained results allowed to successfully distinguish insect particles from other non-insect materials, and to discriminate PAPs particles derived from Tenebrio molitor and Hermetia illucens achieving average Matthews Correlation Coefficient of 0.89 and accuracies exceeding 95% for both tasks. The robust performance and the rapid processing speed of the model enabled to couple it to a digital microscope for real-time automated particle classification, including confidence scores for individual particle predictions. This artificial intelligence model for microscopic identification of complex structures opens new perspectives for official controls achieving performance scores never reached before. It provides to microscopists an optimisation of image analysis and a valuable decision-support tool in complex feed matrices where human expertise is frequently challenged.