SUMMARY. The growth of modern poultry farming in the US has been relentless. The application of novel and innovative technologies from rearing to processing ha

The food industry has experienced rapid growth over the past two decades, driven by technological advancements that have generated vast quantities of complex data. However, the industry’s ability to effectively analyze and leverage this data remains limited due to the lack of control over diverse variables. This review addresses a critical gap by exploring how AI-ML-based approaches can be applied to solve key challenges in the food sector.

Woody breast (WB) myopathy in modern broilers is causing major meat quality issues and consumer complaints. The poultry industry is sorting out WB filets through the inconsistent manual hand-palpation method. Bioelectrical impedance analysis (BIA) method was evaluated as a rapid and objective WB detection method. Freshly deboned broiler breast filets (15 filets × 2 categories × 3 trials) were sorted (hand-palpation) into severe woody (SW) and normal (N) categories were analyzed for BIA values, cook loss, texture (BMORS) method. SW filets had significantly (P < 0.05) higher resistance and reactance compared to N indicating BIA can be used to detect WB filets. In another experiment, we determined the ability of the BIA to differentiate between four WB severity levels using the whole filet. Significant differences were observed in resistance and reactance of normal and other WB categories, however, there were no significant differences among mild, moderate and severe WB categories. Segmental BIA of those filets indicated that BIA can be used to separate cranial, medial and caudal region of the breast filet based on the presence of WB myopathy. Accidental discovery of spaghetti breast in the samples demonstrated the significance of compounding different factors in analyzing WB meat using BIA.

Bioelectrical impedance analysis (BIA) was established to quantify diverse cellular characteristics. This technique has been widely used in various species, such as fish, poultry, and humans for compositional analysis. This technology was limited to offline quality assurance/detection of woody breast (WB); however, inline technology that can be retrofitted on the conveyor belt would be more helpful to processors. Freshly deboned (n = 80) chicken breast fillets were collected from a local processor and analyzed by hand-palpation for different WB severity levels. Data collected from both BIA setups were subjected to supervised and unsupervised learning algorithms. The modified BIA showed better detection ability for regular fillets than the probe BIA setup. In the plate BIA setup, fillets were 80.00% for normal, 66.67% for moderate (data for mild and moderate merged), and 85.00% for severe WB. However, hand-held BIA showed 77.78, 85.71, and 88.89% for normal, moderate, and severe WB, respectively. Plate BIA setup is more effective in detecting WB myopathies and could be installed without slowing the processing line. Breast fillet detection on the processing line can be significantly improved using a modified automated plate BIA.

1. This article reviews the studies on optical technologies for automated poultry carcass inspection, discusses challenges and potential solutions in their real-time applications in poultry slaughter facilities. 2. Over the past few decades, extensive research has been underway to develop an optical technology-based machine vision system for automated inspection of poultry carcasses and viscera. Such an automated technology will not only aid in carcass inspection to maximise food safety, but it will also support the U.S. New Poultry Inspection System’s aim to foster innovation in poultry processing as well as increase line speed. 3. Many earlier studies based on visible and near-infrared spectroscopy showed promise, but could not be implemented successfully in an on-line poultry processing plant. Currently, multi- and hyper-spectral imaging-based machine vision systems have shown promising outcomes. 5. The critical hurdles for real-time application of automated imaging technology in poultry carcass inspection include high-speed processing lines, slaughter facilities environment and variation in broiler rearing practices. Therefore, further improvement in imaging and machine vision technologies based on physiochemical properties on poultry carcasses, the establishment of more technology friendly inspection station, and an integrated data management for different rearing practices are essential to overcome those hurdles.

Technologies for rapid identification and prediction of food spoilage can be crucial in minimizing food waste and losses, although their efficiency requires further improvement. This study aimed to pinpoint specific near-infrared (NIR) wavelengths that could indicate spoilage in raw chicken breast fillets. In this study, commercial tray-packs of boneless, skinless chicken breast fillets stored in a walk-in cooler at 4 °C were periodically tested every other day until they reached the spoilage state (identified by > 7 log CFU/ml). A portable Hyper spectral spectroscopy device (Field Spec Hi-Res4), with a range of wavelengths of 350–2500 nm, was used to measure reflectance. In addition to hyper-spectral analysis, aerobic plate counts were conducted on the fillets. The data from these counts were then used to train a Back Propagation Neural Network (B.P.N.N.) with specific parameters (250,000 steps, a learning rate of 0.02, and 5 hidden layers) and Linear-Support Vector machines (SVM-Linear) with ten-fold cross-validation technique to categorize spoilage into three stages: baseline microbial count (up to 3 log CFU/ml) (Initiation), propagation (between 3 and 6.9 log CFU/ml), and spoiled (> 7 log CFU/ml). The feature extraction process successfully identified the most representative signature wavelengths of 385 nm, 400 nm, 432 nm, 1141 nm, 1321 nm, 1374 nm, 2241 nm, 2292 nm, 2311 nm, and 2412 nm from the whole hyper-spectral profile, which facilitated the classification of different phases of spoilage. The BPNN model demonstrated a high classification accuracy, with 93.7% for baseline counts, 95.2% for the propagation phase, and 98% for the spoiled category. These signature hyperspectral wavelengths hold the potential for developing cost-effective and rapid food spoilage detection systems, particularly for perishable items.