Artificial intelligence applied to enzymatic hydrolysis of lactose: improving the control of industrial processes

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Abstract
Lactose is the main carbohydrate in milk, and its absorption occurs via enzymatic hydrolysis, generating glucose and galactose. Lactose intolerance is the reduction of intestinal hydrolysis capacity due to hypolactasia, which results in the need to consume dairy foods with low levels of this carbohydrate. β-galactosidase enzymes are used in dairy industries to hydrolyze lactose, thereby allowing intolerant consumers access to dairy products without the negative health implications. Alternative and official analytical methods are used to quantify the carbohydrate content resulting from enzymatic hydrolysis. The objective of this study was to evaluate the enzymatic hydrolysis of two distinct industrial enzymes produced by the microorganisms Bacillus licheniformis and Kluyveromyces lactis using three analytical methods: enzymatic method, cryoscopy, and high performance liquid chromatography (HPLC) using artificial intelligence to improve the control of the industrial processes. After adding the enzymes to skim milk, time kinetics was performed by collecting samples at time 0, every 10 min for 1 h, and every 30 min until the end of 5 h of hydrolysis. In 97% of the cases, a decrease in lactose concentration was observed by HPLC, followed by the deepening of the cryoscopic point. Glucose measurements by absorbance and HPLC quantification were correlated (r = 0.79; p < 0.01) but not concordant (p < 0.01). It was concluded that by means of artificial intelligence, it was possible to indirectly estimate lactose concentration using an algorithm that associates cryoscopy and glucose concentration.

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