Comparing human milk macronutrients measured using analyzers based on mid-infrared spectroscopy and ultrasound and the application of machine learning in data fitting
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 co...
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| Format: | Article |
| Language: | English |
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BMC
2022-07-01
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| Series: | BMC Pregnancy and Childbirth |
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| Online Access: | https://doi.org/10.1186/s12884-022-04891-w |
| _version_ | 1811218273990082560 |
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| author | Huijuan Ruan Qingya Tang Yajie Zhang Xuelin Zhao Yi Xiang Yi Feng Wei Cai |
| author_facet | Huijuan Ruan Qingya Tang Yajie Zhang Xuelin Zhao Yi Xiang Yi Feng Wei Cai |
| author_sort | Huijuan Ruan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T07:07:04Z |
| format | Article |
| id | doaj.art-45b66dd37cf04fad8685809af09c5c62 |
| institution | Directory Open Access Journal |
| issn | 1471-2393 |
| language | English |
| last_indexed | 2024-04-12T07:07:04Z |
| publishDate | 2022-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pregnancy and Childbirth |
| spelling | doaj.art-45b66dd37cf04fad8685809af09c5c622022-12-22T03:42:45ZengBMCBMC Pregnancy and Childbirth1471-23932022-07-0122111110.1186/s12884-022-04891-wComparing human milk macronutrients measured using analyzers based on mid-infrared spectroscopy and ultrasound and the application of machine learning in data fittingHuijuan Ruan0Qingya Tang1Yajie Zhang2Xuelin Zhao3Yi Xiang4Yi Feng5Wei Cai6Department of Clinical Nutrition, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong UniversityDepartment of Clinical Nutrition, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghai Key Laboratory of Pediatric Gastroenterology and NutritionDepartment of Clinical Nutrition, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong UniversityDepartment of Clinical Nutrition, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong UniversityDepartment of Clinical Nutrition, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghai Key Laboratory of Pediatric Gastroenterology and NutritionAbstract 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.https://doi.org/10.1186/s12884-022-04891-wHuman milk analyzerMid-infrared spectroscopyUltrasoundBland–Altman methodMachine learning |
| spellingShingle | Huijuan Ruan Qingya Tang Yajie Zhang Xuelin Zhao Yi Xiang Yi Feng Wei Cai Comparing human milk macronutrients measured using analyzers based on mid-infrared spectroscopy and ultrasound and the application of machine learning in data fitting BMC Pregnancy and Childbirth Human milk analyzer Mid-infrared spectroscopy Ultrasound Bland–Altman method Machine learning |
| title | Comparing human milk macronutrients measured using analyzers based on mid-infrared spectroscopy and ultrasound and the application of machine learning in data fitting |
| title_full | Comparing human milk macronutrients measured using analyzers based on mid-infrared spectroscopy and ultrasound and the application of machine learning in data fitting |
| title_fullStr | Comparing human milk macronutrients measured using analyzers based on mid-infrared spectroscopy and ultrasound and the application of machine learning in data fitting |
| title_full_unstemmed | Comparing human milk macronutrients measured using analyzers based on mid-infrared spectroscopy and ultrasound and the application of machine learning in data fitting |
| title_short | Comparing human milk macronutrients measured using analyzers based on mid-infrared spectroscopy and ultrasound and the application of machine learning in data fitting |
| title_sort | comparing human milk macronutrients measured using analyzers based on mid infrared spectroscopy and ultrasound and the application of machine learning in data fitting |
| topic | Human milk analyzer Mid-infrared spectroscopy Ultrasound Bland–Altman method Machine learning |
| url | https://doi.org/10.1186/s12884-022-04891-w |
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