Comparing human milk macronutrients measured using analyzers based on mid-infrared spectroscopy and ultrasound and the application of machine learning in data fitting

Springer Science and Business Media LLC
Han‐Li Ruan1, Qingya Tang1, Yajie Zhang2,3, Xuelin Zhao1, Yi Xiang1, Yi Feng1, Wei Cai3
1Department of Clinical Nutrition, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
2Shanghai Institute of Pediatric Research, Shanghai, China
3Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai, China

Tóm tắt

Abstract Objective Fat, carbohydrates (mainly lactose) and protein in breast milk all provide indispensable benefits for the growth of newborns. The only source of nutrition in early infancy is breast milk, so the energy of breast milk is also crucial to the growth of infants. Some macronutrients composition in human breast milk varies greatly, which could affect its nutritional fulfillment to preterm infant needs. Therefore, rapid analysis of macronutrients (including lactose, fat and protein) and milk energy in breast milk is of clinical importance. This study compared the macronutrients results of a mid-infrared (MIR) analyzer and an ultrasound-based breast milk analyzer and unified the results by machine learning. Methods This cross-sectional study included breastfeeding mothers aged 22–40 enrolled between November 2019 and February 2021. Breast milk samples (n = 546) were collected from 244 mothers (from Day 1 to Day 1086 postpartum). A MIR milk analyzer (BETTERREN Co., HMIR-05, SH, CHINA) and an ultrasonic milk analyzer (Honɡyanɡ Co,. HMA 3000, Hebei, CHINA) were used to determine the human milk macronutrient composition. A total of 465 samples completed the tests in both analyzers. The results of the ultrasonic method were mathematically converted using machine learning, while the Bland-Altman method was used to determine the limits of agreement (LOA) between the adjusted results of the ultrasonic method and MIR results. Results The MIR and ultrasonic milk analyzer results were significantly different. The protein, fat, and energy determined using the MIR method were higher than those determined by the ultrasonic method, while lactose determined by the MIR method were lower (all p < 0.05). The consistency between the measured MIR and the adjusted ultrasound values was evaluated using the Bland-Altman analysis and the scatter diagram was generated to calculate the 95% LOA. After adjustments, 93.96% protein points (436 out of 465), 94.41% fat points (439 out of 465), 95.91% lactose points (446 out of 465) and 94.62% energy points (440 out of 465) were within the LOA range. The 95% LOA of protein, fat, lactose and energy were - 0.6 to 0.6 g/dl, -0.92 to 0.92 g/dl, -0.88 to 0.88 g/dl and - 40.2 to 40.4 kj/dl, respectively and clinically acceptable. The adjusted ultrasonic results were consistent with the MIR results, and LOA results were high (close to 95%). Conclusions While the results of the breast milk rapid analyzers using the two methods varied significantly, they could still be considered comparable after data adjustments using linear regression algorithm in machine learning. Machine learning methods can play a role in data fitting using different analyzers.

Từ khóa


Tài liệu tham khảo

Lemons JA, Moye L, Hall D, Simmons M. Differences in the Composition of Preterm and Term Human Milk during Early Lactation. Pediatr Res 1982; 16: 113–117.

Seliga-Siwecka J, Chmielewska A, Jasińska K. Effect of targeted vs standard fortification of breast milk on growth and development of preterm infants (≤ 32 weeks): study protocol for a randomized controlled trial. Trials 2020; 21: 946.

Boyce C, Watson M, Lazidis G, et al. Preterm human milk composition: a systematic literature review. Br J Nutr 2016; 116: 1033–1045.

Mangili G, Garzoli E. Feeding of preterm infants and fortification of breast milk. Pediatr Med Chir 2017; 39: 158.

Brown JVE, Embleton ND, Harding JE, McGuire W. Multi‐nutrient fortification of human milk for preterm infants. Cochrane Database Syst Rev. 2016;(5). Art. No.: CD000343. https://doi.org/10.1002/14651858.CD000343.pub3. Accessed 11 July 2022.

Garwolińska D, Hewelt-Belka W, Kot-Wasik A, Sundekilde UK. Nuclear magnetic resonance metabolomics reveals qualitative and quantitative differences in the composition of human breast milk and milk formulas. Nutrients. 2020;12:921.

Garwolińska D, Namieśnik J, Kot-Wasik A, Hewelt-Belka W. State of the art in sample preparation for human breast milk metabolomics—merits and limitations. Trac Trends Anal Chem. 2019;114:1–10.

Praticò G, Capuani G, Tomassini A, et al. Exploring human breast milk composition by NMR-based metabolomics. Nat Prod Res. 2014;28:95–101.

Bardanzellu F, Peila C, Fanos V, Coscia A. Clinical insights gained through metabolomic analysis of human breast milk. Expert Rev Proteomics 2019; 16: 909–932.

Cesare Marincola F, Dessì A, Corbu S, et al. Clinical impact of human breast milk metabolomics. Clin Chim Acta 2015; 451: 103–106.

Choi A, Fusch G, Rochow N, et al. Establishment of micromethods for macronutrient contents analysis in breast milk. Matern Child Nutr. 2015;11:761–72.

Kala R, Samková E, Hanu O, et al. Milk protein analysis: An overview of the methods – development and application. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 2019; 67: 345–359.

Smilowitz JT, Gho DS, Mirmiran M, et al. Rapid measurement of human milk macronutrients in the neonatal intensive care unit: accuracy and precision of fourier transform mid-infrared spectroscopy. J Hum Lac. 2014;30:180–9.

Fusch G, Kwan C, Kotrri G, Fusch C. "Bed Side” Human Milk Analysis in the Neonatal Intensive Care Unit. Clin Perinatol. 2017;44:209–67.

Miller EM, Aiello MO, Fujita M, et al. Field and laboratory methods in human milk research. Am J Hum Biol 2013; 25: 1–11.

Kwan C, Fusch G, Rochow N, Fusch C. Milk analysis using milk analyzers in a standardized setting (MAMAS) study: A multicentre quality initiative. Clin Nutr 2020; 39: 2121–2128.

Fusch G, Rochow N, Choi A, et al. Rapid measurement of macronutrients in breast milk: How reliable are infrared milk analyzers? Clin Nutr 2015; 34: 465–476.

Jianxing Z. Limitations in clinical application of rapid measurements of macronutrients in human milk. Chin J Perinat Med. 2019;22:441–4.

Billard H, Simon L, Desnots E, et al. Calibration Adjustment of the Mid-infrared Analyzer for an Accurate Determination of the Macronutrient Composition of Human Milk. J Hum Lact 2016; 32: Np19-27.

Biggs DA. Infrared milk analyzer. J Dairy Sci 1972; 55: 650–651.

de Fluiter KS, Kerkhof GF, van Beijsterveldt I, et al. Longitudinal human milk macronutrients, body composition and infant appetite during early life. Clin Nutr 2021; 40: 3401–3408.

García-Lara NR, Escuder-Vieco D, García-Algar O, et al. Effect of freezing time on macronutrients and energy content of breastmilk. Breastfeed Med 2012; 7: 295–301.

He Lianghua zL Li, Nan Deng, Chao. Application Study on Ultrasonic Breast Milk Quality System Detection Method. Comper Meas Control. 2013;21:2648–50.

Wang Jing LX, Guixiong GU. Composition of breast milk and its determination method. Chin J Woman Child Health Res. 2015;26:641–56.

Peng Dan XK, Xin Yin, Wang Hui. Study on Effects of Main Components and Temperature on Ultrasonic Properties in Milk. Food Sci. 2007;28:66–70.

Luo Xiaoming BD, Feng Lijuan. Application of milk analyzer in determining human milk nutritional composition. Chin J Woman Child Health Res. 2014;25:11–3.

Oto Hanuš JŘ, Eva Samková, David Ledvina, Jaroslav Kopecký. A Comparison of Result Reliability for Investigation of Milk Composition by Alternative Analytical Methods in Czech Republic. Acta Universitatis Agriculturae Et Silviculturae Mendelianae Brunensis 2014; 62: 929–937.

Menjo A, Mizuno K, Murase M, et al. Bedside analysis of human milk for adjustable nutrition strategy. Acta Paediatr 2009; 98: 380–384.

Morgenstern JD, Rosella LC, Costa AP, et al. Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology. Adv Nutr 2021; 12: 621–631.

Buchlak QD, Esmaili N, Leveque JC, et al. Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review. J Clin Neurosci 2021; 89: 177–198.

Ikemura K, Bellin E, Yagi Y, et al. Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study. J Med Internet Res 2021; 23: e23458.

Hanko M, Grendár M, Snopko P, et al. Random Forest-Based Prediction of Outcome and Mortality in Patients with Traumatic Brain Injury Undergoing Primary Decompressive Craniectomy. World Neurosurg 2021; 148: e450-e458.

Yang L, Wu H, Jin X, et al. Study of cardiovascular disease prediction model based on random forest in eastern China. Sci Rep 2020; 10: 5245.

Handelman GS, Kok HK, Chandra RV, et al. eDoctor: machine learning and the future of medicine. J Intern Med 2018; 284: 603–619.

Ruan H, Tang Q, Zhao X, et al. The levels of osteopontin in human milk of chinese mothers and its associations with maternal body composition. Food Sci Hum Wellness. 2022;11:1419–27.

Villalpando S, del Prado M. Interrelation among dietary energy and fat intakes, maternal body fatness, and milk total lipid in humans. J Mammary Gland Biol Neoplasia 1999; 4: 285–295.

Slutzah M, Codipilly CN, Potak D, et al. Refrigerator storage of expressed human milk in the neonatal intensive care unit. J Pediatr 2010; 156: 26–28.

Yang T, Zhang Y, Ning Y, et al. Breast milk macronutrient composition and the associated factors in urban Chinese mothers. Chin Med J. 2014;127:1721–5.

Zhu M, Yang Z, Ren Y, et al. Comparison of macronutrient contents in human milk measured using mid-infrared human milk analyser in a field study vs. chemical reference methods. Matern Child Nutr. 2017;13. https://doi.org/10.1111/mcn.12248.

Hongo K, Maezono R, Miura K. Random number generators tested on quantum Monte Carlo simulations. J Comput Chem 2010; 31: 2186–2194.

Sotomayor CG, Benjamens S, Dijkstra H, et al. Introduction of the grayscale median for ultrasound tissue characterization of the transplanted kidney. Diagnostics (Basel). 2021;11:390.

Stamenovic D, Schneider T, Messerschmidt A. Aspirin for patients undergoing major lung resections: hazardous or harmless?†. Interact Cardiovasc Thorac Surg 2019; 28: 535–541.

Gwon H, Ahn I, Kim Y, et al. Self-Training With Quantile Errors for Multivariate Missing Data Imputation for Regression Problems in Electronic Medical Records: Algorithm Development Study. JMIR Public Health Surveill 2021; 7: e30824.

Ruan H, Tang Q, Yang Q, et al. Resting energy expenditure early after cardiac surgery and validity of predictive equations: a prospective observational study. Ann Nutr Metab. 2021;77:271–8.

Flegal KM, Graubard B, Ioannidis JPA. Use and reporting of Bland-Altman analyses in studies of self-reported versus measured weight and height. Int J Obes (Lond) 2020; 44: 1311–1318.

Cao X, Bai Z, Sun C, et al. Bland-Altman Analysis of Different Radiographic Measurements of the Hallux Valgus Angle and the Intermetatarsal Angle After Distal Osteotomy. Orthop Surg 2020; 12: 1621–1626.

Thajer A, Fusch G, Binder C, et al. Human milk analyser underestimated protein content of unfortified and fortified samples compared to elemental analysis. Acta Paediatr 2019; 108: 2298–2300.

Toffanin V, Marchi MD, Lopez-Villalobos N, Cassandro M. Effectiveness of mid-infrared spectroscopy for prediction of the contents of calcium and phosphorus, and titratable acidity of milk and their relationship with milk quality and coagulation properties. Int Dairy J. 2015;41:68–73.

De Marchi M, Toffanin V, Cassandro M, Penasa M. Prediction of coagulating and noncoagulating milk samples using mid-infrared spectroscopy. J Dairy Sci 2013; 96: 4707–4715.

McParland S, Banos G, McCarthy B, et al. Validation of mid-infrared spectrometry in milk for predicting body energy status in Holstein-Friesian cows. J Dairy Sci 2012; 95: 7225–7235.

McParland S, Kennedy E, Lewis E, et al. Genetic parameters of dairy cow energy intake and body energy status predicted using mid-infrared spectrometry of milk. J Dairy Sci 2015; 98: 1310–1320.

Thông tin
Thông tin xuất bản

Springer Science and Business Media LLC

Thông tin tác giả