Poly (β-L-malic acid) (PMLA) is a biopolymer with promising applications in the food and medical fields.However, its industrial development has been constrained by the lack of mature downstream separation and purification techniques.In this study, the one-factor-at-a-time (OFAT) method was employed to optimize the decolorization of PMLA following resin-based desorption.The experimental results revealed a significant trade-off relationship between the decolorization rate and the retention rate of PMLA, highlighting the necessity for dual-objective optimization.To address this, a backpropagation artificial neural network (BP-ANN) combined with the non-dominant sorting genetic algorithm-Ⅱ (NSGA-Ⅱ) was introduced to systematically explore the multi-objective optimization solution set for the activated carbon decolorization of PMLA.The optimal process conditions were determined as follows, activated carbon dosage of 5.0-5.3 g/L, pH 8, processing time of 100 min, and temperature of 50 ℃.Under these optimized conditions, the decolorization rate of PMLA was 73%-77% (with color comparable to that of deionized water), while the retention rate remained at 93%-96%.This study not only extends the application of BP-ANN and NSGA-Ⅱ in the purification of PMLA but also provides a valuable reference for the further development of interpretable machine learning and intelligent optimization algorithms in the field of natural product extraction.
YANG Shuyu
,
LIU Xiaoqian
,
CAO Weifeng
,
QIAO Changsheng
. Decolorization process of poly(β-L-malic acid) using one-factor-at-a-time experiments integrated with artificial neural network and genetic algorithm[J]. Food and Fermentation Industries, 2026
, 52(16)
: 51
-59
.
DOI: 10.13995/j.cnki.11-1802/ts.044893
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