Rapid Assessment of Quality Parameters in Cocoa Butter Using ATR-MIR Spectroscopy and Multivariate Analysis

The development of sensitive and robust screening tool(s) for assuring the quality of incoming raw materials would supplement the assurances provided by food manufacturer vendor auditing programs. Our aim was to evaluate the ability of attenuated total reflectance mid‐infrared (ATR‐MIR) spectroscopy in combination with multivariate analysis as a screening tool for the diverse cocoa butter supply. Forty different cocoa butter samples encompassing an acceptable range of compositional variability for the chocolate industry were included. Cocoa butters were characterized for their melt characteristics (melting heat), triacylglycerol content and fatty acid composition (GC‐FAME). Soft independent modeling of class analogy (SIMCA) and partial least squares regression (PLSR) were used for classification and quantification analysis. SIMCA classified all cocoa butters in distinct clusters in a 3‐dimensional space but no sample clustering patterns were associated with melt characteristics. Spectral differences responsible for the separation of classes were attributed to stretching vibrations of the ester (–C=O) linkage (1,660–1,720 cm^?1). PLSR models showed correlation coefficients >0.93 and prediction errors (SECV) of 1.5 units for melt characteristics, 0.2–0.3 and 0.4–0.8 % for major fatty acids and triacylglycerols, respectively. ATR‐MIR spectroscopy combined with pattern recognition analysis provides robust models for characterization and determination of cocoa butter composition.