Biochemical numerical identification method of lactic acid bacterial species

Abstract

The present application belongs to the field of microorganism identification, and discloses a biochemical numerical identification method of lactic acid bacteria strains. The present application provides a biochemical numerical identification method for identifying 44 species / subspecies of lactic acid bacteria, including a biochemical reaction positive probability database and a numerical identification kit of 47 biochemical combinations. The lactic acid bacteria biochemical reaction positive rate database in the present application is obtained by statistically analyzing a large number of isolated strains in the laboratory, conducting biochemical experiments and collecting lactic acid bacteria biochemical data from Bac Dive website, thereby ensuring the accuracy and effectiveness of the data. Meanwhile, the present application is used to identify 14 lactic acid bacteria standard strains and 171 lactic acid bacteria isolated strains, and the accuracy rate of the results reaches 98.92%.

Description

Technical Field

[0001] This invention belongs to the field of microbial identification, specifically relating to a biochemical numerical identification method for lactic acid bacteria strains. Background Technology

[0002] Lactic acid bacteria (LAB) is a collective term for a group of bacteria that can ferment carbohydrates to produce large amounts of lactic acid. Taxonomically, lactic acid bacteria mainly belong to the phylum Firmicutes (Lactobacillus). Firmicutes ) — Bacillus class ( Bacilli — Lactobacilli ( Lactobacilli — Lactobacillus family ( Lactobacillaceae These bacteria are Gram-positive, non-spore-forming, mostly non-motile, and facultatively anaerobic. Lactic acid bacteria are widely distributed in nature, commonly found in the intestines of mammals, dairy products, fermented plant products, and other environments. They are an important component of the normal gut microbiota in humans, playing various physiological roles such as maintaining microecological balance, promoting nutrient absorption, and regulating immunity. It is worth noting that, with very few exceptions, lactic acid bacteria are generally harmless to humans, and most strains are considered beneficial probiotics.

[0003] Currently, in the field of lactic acid bacteria identification, molecular typing techniques and MALDI-TOF MS are often limited in application due to their high cost, complex operation, and reliance on expensive instruments. Many small and medium-sized enterprises still mainly rely on traditional phenotypic identification methods. However, traditional biochemical identification tests are cumbersome, lack integrated operation, and often introduce subjective errors in result interpretation due to unclear reactions or cross-reactions, affecting the accuracy of identification. Furthermore, although imported commercial lactic acid bacteria biochemical identification systems have a high degree of automation, they are expensive, and their accompanying strain databases often fail to keep up with the latest taxonomic developments in lactic acid bacteria, making it difficult to cover the rich native lactic acid bacteria resources in traditional fermented foods in my country. Therefore, developing a numerical identification system for lactic acid bacteria with independent intellectual property rights can not only fill a market gap and provide a cost-effective detection solution, but also has significant implications for food development. This study addresses the shortcomings of existing systems, such as outdated classification standards and high prices, by developing a lactic acid bacteria identification system based on a combination of biochemical reactions and numerical theory, providing an efficient and low-cost solution for clinical and food safety applications. Summary of the Invention

[0004] The purpose of this invention is to overcome at least one deficiency of the prior art and provide a biochemical numerical identification method for lactic acid bacteria strains.

[0005] The technical solution adopted in this invention is: In a first aspect, the present invention provides a numerical identification method for lactic acid bacteria strains, comprising the following steps: Biochemical reaction experiments were conducted on different known lactic acid bacteria species. The biochemical reaction experiments included the following 47 types of biochemical reactions: D-lactose, D-cellobiose, D-merinobiose, D-minotriose, D-raffinose, L-rhamnose, D-minobiose, eugenol, D-calendol, D-glucose, D-arabinose, D-fructose, amygdalin, glycogen, erythritol, L-xylose, D-arabinol, L-arabinol, glycine, D-trehalose, D-xylose, mannitol, and yamylose. Biochemical experiments on pear alcohol, starch, D-sucrose, D-galactose, D-maltose, D-mannose, inulin, L-arabinose, D-ribose, L-sorbose, methyl-α-D-glucopyranoside, N-acetylglucosamine, salicin, arbutin, D-tagatose, potassium gluconate, potassium 5-ketogluconate, inositol, methyl-α-D-mannopyranoside, methyl-β-D-xylopyranoside, xylitol, N-acetyl-d-galactosamine, lythose, α-cyclodextrin, and aesculin; The biochemical reaction positivity rates of different types of lactic acid bacteria were obtained, and different known types of lactic acid bacteria were used as different taxonomic units to determine the probability matrix of each taxonomic unit. Calculate the pattern frequency of the classification unit Total frequency of occurrence The total occurrence frequency S, the identification percentage ID, and the pattern likelihood ratio T are multiple factors, where: mode frequency It is the product of the maximum total probability of occurrence of biochemical experimental combinations under this classification unit. In the formula: LJ represents the probability of taxonomic unit J occurring in all biochemical tests. X represents the probability of a certain biochemical test occurring for classification unit J. i For the i-th biochemical experiment; Total frequency of occurrence This is the product of the actual occurrence probabilities of a given unknown bacterium in the biochemical test combinations within this taxonomic unit. ; The total occurrence frequency S is the sum of the total occurrence frequencies of each classification unit. ; The identification percentage ID is calculated by dividing the total frequency of each classification unit by the total frequency of multiple items, then multiplying by 100%. The sum of all identification percentages should equal 100%. ; The pattern likelihood ratio T is calculated by dividing the total frequency of each class unit by the pattern frequency of that class unit, and then multiplying by 100%. ; After the bacterial strains to be identified were cultured in a selective culture medium for lactic acid bacteria, biochemical reaction experiments were performed. Based on the positive probability data of the lactic acid bacteria biochemical reactions, the negative / positive test results were converted into probability values, and the pattern frequency of the bacterial strains to be identified was calculated. Total frequency of occurrence Multiple total occurrence frequency S, identification percentage ID, and pattern likelihood ratio T; Based on the identification percentage (ID) and pattern likelihood ratio (T) of the strain to be identified, the reliability of the identification results of the strain to be identified is evaluated, and the taxonomic unit with the highest score is the identification result of the strain to be identified.

[0006] Optionally, the positive probability data of the lactic acid bacteria biochemical reaction is obtained by statistical analysis of the biochemical reaction experiments using known standard strains and isolates of the genus Lactobacillus, or further determined by combining publicly available data.

[0007] Optionally, when converting negative / positive test results into probability values, the probability value of a positive test result is the known positive probability value, and the probability value of a negative test result is (100% - the known positive probability value).

[0008] Optionally, to avoid extreme values, if the probability value is 0% or 100%, a value close to 0 or close to 100% can be used instead.

[0009] Optionally, a value close to >0 is 1%, and a value close to <100% is 99%.

[0010] Optionally, the lactic acid bacteria strains include the following 44 strains: Pediococcus lactis ( Pediococcus lactic acid ), Pediococcus pentosaceus ( Pediococcus pentosaceus Lactobacillus plantarum ( Lactiplantibacillus plantarum ), fermenting lactobacillus ( Limosilactobacillus leaven ), Lactobacillus brevis ( Levilactobacillus brevis ), Leuconostoc membranaceus ( Leuconostoc mesenteric ), Leuconostoc membranaceus subsp. glucanoides ( Leuconostoc mesenteric subsp. right-handed ), Leuconostoc membranaceus subsp. milk fat ( Leuconostoc mesenteric subsp. cream Leuconostoc mesenteroides ( Leuconostoc lactis ), Lactobacillus casei ( Lactobacillus paracasei ), Lactobacillus casei subsp. tough ( Lactobacillus cheese subsp. tolerant ), Lactococcus lactis ( Lactococcus lactis ), Lactobacillus pentosolicus ( Lactiplantibacillus pentosus), Clostridium perfringens ( Carnobacterium divergens ), Weissella greenis ( Weissella viridescens ), confused Weissella ( Weissella confusa ), Streptococcus thermophilus ( Streptococcus thermophilus Leuconostoc mesenteroides ( ), Leuconostoc mesenteroides Leuconostoc citri ), Lactobacillus brucellosis ( Lentilactobacillus buchneri Lactobacillus helveticus ( Lactobacillus helveticus Lactobacillus delbrueckii subsp. bulgaricus ( Lactobacillus delbrueckii subsp. Bulgarian Lactobacillus delbrueckii subsp. delbrueckii ( Lactobacillus delbrueckii subsp. delbrueckii Lactobacillus delbrueckii subsp. lactis ( Lactobacillus delbrueckii subsp. milk ), Curved Lactobacillus ( Latilactobacillus curvatus ), Lactobacillus acidophilus ( Lactobacillus acidophilus Lactobacillus rhamnosus ( Lactobacillus rhamnosus ), Green balloon bacteria ( Aerococcus viridans ), Heat-killing Sossylvatica ( Brochothrix thermophacta ), Maltodactylum indicum ( Carnobacterium maltaromaticum ), hill-shaped secondary fermentation lactobacillus ( Secundilactobacillus hilly ), Lactobacillus fructose-loving fruit ( Fructilactobacillus fructivorans ), Lactobacillus lindneri ( Fructilactobacillus lindneri ), salivary host-associated lactobacillus ( Ligilactobacillus salivary ), Lactococcus raffinis ( Pseudolactococcus raffinolactis ), harmful Pediococcus ( Pediococcus harmfulus ), Halophilic Tetracoccus ( Tetragenococcus halophilus Lactobacillus curvature () Lactobacillus crispatus ), Leuconostoc membranaceus subsp. enterica ( Leuconostoc mesenteroides subsp. mesenteroides ), Lactobacillus brucellosis ( Lentilactobacillus parabuchneri Sake is widely covered with Lactobacillus ( Latilactobacillus sakei Lactobacillus subsp. fleshy in sake ( ) Latilactobacillus sakei subsp. carnosus Lactobacillus sacchariformis (Lactobacillus sacchariformis subsp. sake Latilactobacillus sakei subsp. sakei Lactobacillus casei ( Lacticaseibacillus casei ), Lactococcus fatii ( Lactococcus cremoris ).

[0011] Optionally, the test strains can be obtained from food samples or environmental samples.

[0012] Optionally, the biochemical tests for inconsistencies between the test strain and each taxonomic unit can be listed separately, and the test strain can be identified and determined using other known methods.

[0013] The beneficial effects of this invention are: (1) The negative or positive identification results of the biochemical reaction involved in this invention are clearly defined, easy to distinguish and simple to operate, requiring only 4 sample additions, which is significantly better than similar commercial products at home and abroad (requiring 50 sample additions). (2) This invention achieves effective differentiation of 44 species / subspecies of lactic acid bacteria, which is superior to similar commercial products at home and abroad (37 species / subspecies). (3) The principles and data sources used in the identification of this invention are reliable, and the accuracy of the identification results is as high as 98.92%, which is significantly better than similar commercial products at home and abroad (43.64%). Detailed Implementation

[0014] The following disclosure provides many different implementations or examples for different ways of implementing the present invention. Example 1: Identifying the types of lactic acid bacteria

[0015] The 44 types of lactic acid bacteria identified in this embodiment are shown in Table 1 below.

[0016] Table 1 Pediococcus acidilactici Pediococcus pentosaceus Lactobacillus plantarum Fermented Lactobacillus mucinus Lactobacillus brevis Leuconostoc membranaceus Leuconostoc membranaceus subsp. glucan Leuconostoc membranaceus subsp. milk fat Leuconostoc membranaceus subsp. enterica Leuconostoc mesenteroides Lactobacillus casei Lactobacillus casei subsp. tough Lactococcus lactis Lactococcus fat Pentosacchari lactobacillus Clostridium perfringens Weisslerae green Confused Weissella Streptococcus thermophilus Lemon-colored Leuconostoc Lactobacillus brucellosis Lactobacillus brevicornu Lactobacillus helveticus Lactobacillus delbrueckii subsp. bulgaricus Lactobacillus delbrueckii subsp. delbrueckii Lactobacillus delbrueckii subsp. lactis Curved Lactobacillus Lactobacillus acidophilus Lactobacillus rhamnosus Sake contains a wide variety of lactobacilli Sake is widely distributed with Lactobacillus subsp. meatus Lactobacillus sacchariformis subsp. sakei is found in sake. Lactobacillus casei Green balloon bacteria Heat-killing Sossyl Malt-containing botulinum toxin Hill-shaped secondary fermentation of Lactobacillus Fructose-loving Lactobacillus Lactobacillus lindneri Saliva host-associated lactobacilli Lactococcus raffinis Harmful Pediococcus Halophilic Tetracoccus Lactobacillus curvature Example 2: Selecting the required biochemical experiments in the numerical method

[0017] Forty-seven biochemical reactions were selected using a non-probabilistic data matrix branch diagram. These reactions are: D-lactose (LAC), D-cellobiose (CEL), D-merinobiose (MEL), D-minotriose (MLZ), D-raffinose (RAF), L-rhamnose (RHA), D-minobiose (TUR), valerol (DUL), D-calendol (ADO), D-glucose (GLU), D-arabinose (DARA), D-fructose (FRU), amygdalin (AMY), glycogen (GLYG), erythritol (ERY), L-xylose (LXYL), D-arabinol (DARL), L-arabinol (LARL), glycine (GLY), D-trehalose (TRE), D-xylose (DXYL), mannitol (MAN), and sorbitol (SOB). Starch (AMD), D-sucrose (SAC), D-galactose (GAL), D-maltose (MAL), D-mannose (MNE), inulin (INU), L-arabinose (LARA), D-ribose (RIB), L-sorbose (SBE), methyl-α-D-glucopyranoside (MDG), N-acetylglucosamine (NAG), salicin (SAL), arbutin (ARB), D-tagatose (TAG), potassium gluconate (GNT), 5-ketogluconate (5KG), inositol (INO), methyl-α-D-mannopyranoside (MDM), methyl-β-D-xylopyranoside (MDX), xylitol (XLT), N-acetyl-d-galactosamine (NDG), lysose (LYX), α-cyclodextrin (DEX), and aesculin (ESC). Example 3: Establishing a Mathematical Model in Numerical Methods

[0018] 1. Establish a positive probability database Based on the existing 197 lactic acid bacteria isolates in our laboratory, the above 47 biochemical experiments were conducted. The positive rates of various biochemical reactions of different bacteria were statistically analyzed based on the results. Combined with the biochemical data of 1729 lactic acid bacteria collected from the BacDive website (https: / / bacdive.dsmz.de / ), a database of positive biochemical reactions of lactic acid bacteria was established for a total of 1926 lactic acid bacteria.

[0019] The details are shown in Table 2: Table 2. Database of Positive Probability of Lactic Acid Bacteria Biochemical Reactions 1 99 11 98 4 99 4 99 99 2 74 2 13 2 99 36 97 57 99 98 99 97 38 79 14 25 3 97 90 95 91 97 97 96 96 91 95 19 95 4 88 56 63 60 90 88 43 4 56 12 2 1 5 86 25 68 12 83 91 3 2 71 1 0 2 6 92 60 59 92 92 91 92 62 83 92 41 17 7 100 31 40 91 100 95 100 31 95 84 9 0 8 100 0 100 50 0 0 0 0 0 0 0 0 9 90 70 55 83 53 91 54 9 57 13 0 2 10 99 75 99 87 99 88 99 94 1 99 65 86 11 79 79 79 60 59 80 79 100 0 99 0 0 12 100 96 100 34 100 100 100 100 0 100 13 17 13 99 99 99 99 99 99 99 99 89 99 50 53 14 100 0 0 100 100 100 100 100 0 100 0 100 15 94 2 6 2 86 94 86 0 0 93 0 0 16 98 0 82 90 98 80 89 80 4 18 0 0 17 78 76 5 78 72 0 53 5 0 2 0 0 18 100 0 5 100 100 96 86 95 0 100 0 3 19 90 42 90 90 87 90 0 0 90 0 19 81 20 93 70 70 13 63 31 86 12 0 63 0 0 21 83 92 2 5 66 6 66 0 0 0 0 0 22 92 0 0 92 92 0 92 0 0 0 0 0 23 98 53 10 97 95 67 80 22 6 87 0 0 24 100 29 100 6 100 83 97 41 3 11 0 0 25 95 82 38 95 95 84 83 77 12 69 1 0 26 95 93 90 44 96 65 96 93 0 95 0 93 27 100 100 100 100 100 29 100 14 0 40 20 0 28 100 0 0 100 100 100 100 100 0 100 0 0 29 100 57 57 100 100 96 100 100 18 100 79 0 30 100 0 99 0 100 100 0 0 0 0 0 0 31 98 0 0 0 88 14 0 0 0 0 0 0 32 100 0 0 0 100 100 0 0 0 0 0 0 33 96 88 77 95 96 95 96 0 96 94 0 0 34 100 100 100 100 100 100 100 100 100 100 50 33 35 100 0 95 0 100 0 100 100 0 88 0 0 36 80 0 80 12 80 76 80 76 28 76 0 0 37 99 80 79 99 99 99 79 91 45 72 0 0 38 100 75 58 100 100 100 100 58 100 100 0 0 39 99 4 99 99 99 99 0 0 99 0 0 99 40 100 82 100 24 100 88 100 82 24 100 0 0 41 93 5 93 88 93 60 93 13 83 88 0 0 42 100 0 100 100 100 83 100 11 100 18 0 0 43 100 78 100 40 100 36 98 98 0 100 27 97 44 100 78 100 33 100 56 100 56 33 56 0 22 Continued from the table above 1 0 0 92 99 97 0 26 0 0 0 99 34 2 38 0 90 99 46 0 34 3 0 1 99 90 3 58 1 80 94 3 9 19 91 88 27 96 94 4 34 2 25 88 37 1 1 2 1 3 6 4 5 3 0 70 95 89 0 0 22 0 40 43 0 6 67 0 88 63 88 1 2 37 3 90 92 45 7 31 0 31 75 31 0 0 31 0 99 91 31 8 0 0 0 0 0 0 0 0 0 0 100 0 9 44 1 34 8 17 0 0 10 0 9 55 9 10 1 1 3 99 2 53 6 99 72 32 99 68 11 0 0 0 98 0 0 0 80 0 0 79 99 12 12 28 15 100 75 0 0 26 0 14 100 62 13 41 0 87 95 89 7 23 99 51 11 99 99 14 0 0 0 100 0 0 0 0 0 0 100 100 15 0 0 0 9 0 0 0 5 0 0 94 0 16 0 0 0 57 95 0 0 0 0 0 77 77 17 13 32 0 2 0 0 0 0 0 0 9 0 18 0 0 86 0 13 0 0 22 0 62 99 38 19 67 0 90 90 90 0 0 19 0 62 0 0 20 0 0 0 0 0 0 0 0 0 0 84 1 21 0 0 0 0 0 0 0 0 0 0 3 0 22 0 0 0 0 0 0 0 0 0 0 33 0 23 17 2 0 0 0 0 0 0 0 0 56 20 24 0 14 3 94 0 0 0 0 0 0 100 6 25 34 3 0 1 0 0 0 3 2 1 80 72 26 1 0 0 96 0 86 87 94 83 82 95 89 27 30 10 0 0 0 0 0 60 40 20 90 0 28 0 0 0 100 0 0 0 86 0 0 100 100 29 18 0 0 100 0 0 0 89 36 89 100 100 30 0 0 50 100 100 0 0 0 0 0 75 0 31 0 0 0 26 0 0 0 0 0 0 0 0 32 0 0 0 0 0 0 0 0 0 0 0 0 33 95 0 0 0 0 0 32 95 92 0 95 0 34 100 100 33 16 83 16 0 66 0 16 100 33 35 0 0 0 0 0 0 0 0 0 0 10 15 36 0 0 84 0 4 0 0 20 12 6 80 76 37 57 68 0 0 0 0 0 21 0 24 87 65 38 100 0 100 67 100 0 0 33 0 100 92 58 39 61 0 99 99 0 0 0 0 0 0 0 0 40 0 0 100 100 12 0 6 76 0 0 100 0 41 0 0 65 93 0 0 0 0 0 0 93 0 42 0 0 83 100 0 0 0 0 0 0 100 0 43 0 0 0 38 0 9 0 100 14 19 100 83 44 33 33 0 56 22 0 0 56 0 0 100 56 Continued from the table above 1 87 78 96 0 0 88 13 2 0 0 0 2 2 95 97 95 0 3 49 3 2 0 0 0 1 3 93 93 98 0 63 5 46 4 0 4 1 66 4 4 3 20 1 2 5 61 27 0 1 0 0 5 4 4 42 0 3 1 62 52 0 0 0 1 6 57 48 80 0 88 1 35 23 0 0 0 0 7 31 31 31 0 90 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 9 7 9 17 0 10 7 3 2 0 0 0 2 10 98 95 98 0 98 99 62 0 0 15 12 3 11 100 100 80 0 0 60 0 0 0 0 0 0 12 100 94 98 2 0 0 28 2 0 0 0 2 13 97 95 99 0 13 0 27 0 0 1 0 2 14 100 100 100 0 0 0 67 0 0 0 0 0 15 0 2 5 0 5 0 11 0 0 0 0 0 16 100 100 89 0 18 0 95 0 0 0 0 0 17 2 0 24 22 27 0 2 3 0 0 0 0 18 64 59 95 0 100 0 63 3 0 0 0 0 19 0 0 67 0 62 0 48 48 0 0 0 0 20 12 1 12 0 0 0 0 0 0 0 0 0 21 5 0 4 0 0 0 0 0 0 0 0 0 22 0 0 8 0 0 0 0 0 0 0 0 0 23 40 31 0 0 0 0 0 0 0 0 0 0 24 3 6 35 0 14 6 0 0 0 0 0 0 25 79 20 95 1 11 3 0 0 0 0 0 0 26 94 93 92 0 83 95 40 0 0 9 27 0 27 14 14 14 0 20 20 0 0 0 0 0 0 28 100 100 100 0 0 64 0 0 0 0 65 0 29 100 100 100 0 29 0 64 25 0 0 36 43 30 0 25 50 0 0 0 100 100 0 0 0 0 31 0 0 43 0 0 0 90 0 0 0 0 0 32 0 0 13 0 0 0 0 0 0 0 0 0 33 38 33 33 0 5 0 0 1 0 0 0 0 34 100 83 100 0 50 1 0 0 0 0 0 0 35 95 80 100 0 0 0 0 0 0 0 0 5 36 76 76 100 0 76 64 0 0 0 0 0 4 37 93 88 96 61 28 13 0 0 0 0 0 0 38 58 42 100 0 100 0 17 17 0 0 0 0 39 0 0 6 0 0 0 61 19 0 0 0 0 40 76 76 88 0 0 0 50 0 0 0 0 0 41 0 0 48 0 0 0 88 0 0 0 0 0 42 2 0 65 0 14 0 18 0 0 0 0 0 43 100 97 98 0 33 92 64 0 0 11 5 0 44 56 56 56 0 0 0 39 0 0 0 0 0 Continued from the table above 1 0 0 0 0 0 0 79 0 0 0 0 0 2 0 0 0 0 0 0 66 0 0 0 0 0 3 0 1 0 0 0 0 0 3 0 8 3 0 4 0 0 0 0 0 0 0 0 0 1 0 0 5 0 0 0 0 18 0 2 0 0 0 0 0 6 0 0 0 1 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 10 0 4 0 13 0 0 85 17 0 0 22 0 11 0 0 0 0 0 0 83 0 0 0 0 0 12 0 0 0 0 0 0 0 0 100 0 0 0 13 0 0 0 0 0 0 0 0 0 23 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 0 0 0 0 0 16 0 0 0 0 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 11 0 0 0 0 0 18 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 4 0 0 0 0 0 0 0 20 0 0 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 24 0 0 0 0 0 0 83 0 0 0 0 0 25 0 0 0 0 0 0 0 0 10 0 0 0 26 1 10 0 2 2 0 98 0 0 0 0 0 27 0 0 0 0 0 0 0 0 0 0 0 0 28 0 0 0 0 0 0 0 0 0 0 0 0 29 0 0 0 0 0 0 0 0 100 0 0 0 30 0 0 0 0 0 0 0 0 0 0 0 0 31 0 0 0 0 0 0 0 0 0 0 0 0 32 0 0 0 0 0 0 0 0 0 0 0 0 33 0 0 0 0 0 5 0 0 0 10 33 0 34 0 0 0 0 0 0 0 0 87 0 33 0 35 0 0 0 0 0 0 0 0 0 0 0 0 36 6 12 0 0 0 0 0 0 0 6 0 0 37 0 0 0 0 0 0 0 0 5 0 0 0 38 0 0 0 0 0 0 0 0 0 0 0 0 39 0 0 0 0 0 0 0 0 0 0 0 0 40 0 0 0 0 0 0 0 0 0 0 0 0 41 0 0 0 0 0 0 50 0 0 0 0 0 42 0 0 0 0 0 0 0 0 0 0 0 0 43 0 2 0 3 0 0 63 2 0 0 0 0 44 0 0 0 0 0 0 0 0 44 0 0 0 Note: The data in the table represent the positive rate (%) of each biochemical reaction. The strains numbered 1-44 in the table are, in order: 1. *Pediococcus lactis*, 2. *Pediococcus pentosaccharis*, 3. *Lactobacillus plantarum*, 4. *Lactobacillus fermentum*, 5. *Lactobacillus brevis*, 6. *Leuconostoc membranaceus*, 7. *Leuconostoc membranaceus* subsp. *glucan*, 8. *Leuconostoc membranaceus* subsp. *milk fat*, 9. *Leuconostoc membranaceus* subsp. *membranaceus*, 10. *Leuconostoc membranaceus*, 11. *Lactobacillus casei*, 12. *Lactobacillus casei* subsp. *tough*, 13. *Lactococcus lactis*, 14. *Lactococcus milk fat*, 15. *Lactobacillus pentosaccharis*, 16. *Clostridium perfringens*, 17. *Weissella viride*, 18. *Weissella confounding*, 19. *Streptococcus thermophilus*, 20. *Leuconostoc citrinum*, 21. *Lactobacillus brucelli*, 22. *Lactobacillus brucelli*, 23. *Russula spp.* Lactobacillus spp., 24. Lactobacillus delbrueckii subsp. bulgaricus, 25. Lactobacillus delbrueckii subsp. delbrueckii, 26. Lactobacillus delbrueckii subsp. lactis, 27. Lactobacillus curvilinearis, 28. Lactobacillus acidophilus, 29. Lactobacillus rhamnosus, 30. Lactobacillus sakei, 31. Lactobacillus sakei subsp. flesh, 32. Lactobacillus sakei subsp. sakei, 33. Lactobacillus casei, 34. Lactobacillus viridans, 35. Lactobacillus heat-killing, 36. Lactobacillus maltodactylis, 37. Lactobacillus hillockii, 38. Lactobacillus fructose-loving, 39. Lactobacillus lindneri, 40. Lactobacillus salivary host-associated, 41. Lactococcus raffinis, 42. Pediococcus maltophilia, 43. Halophilic tetrazoococcus, 44. Lactobacillus curvilinearis; The data in the table represent the percentage positive rate of each biochemical reaction. The names and abbreviations of the biochemical reactions are as follows: D-lactose is LAC, D-cellobiose is CEL, D-merinobiose is MEL, D-minotriose is MLZ, D-raffinose is RAF, L-rhamnose is RHA, D-minobiose is TUR, eugenol is DUL, D-calendol is ADO, D-glucose is GLU, D-arabinose is DARA, D-fructose is FRU, amygdalin is AMY, glycogen is GLYG, erythritol is ERY, L-xylose is LXYL, D-arabinol is DARL, L-arabinol is LARL, glycerol is GLY, D-trehalose is TRE, D-xylose is DXYL, mannitol is MAN, sorbitol is SO B. Starch is AMD, D-sucrose is SAC, D-galactose is GAL, D-maltose is MAL, D-mannose is MNE, inulin is INU, L-arabinose is LARA, D-ribose is RIB, L-sorbose is SBE, methyl-αD-glucopyranoside is MDG, N-acetylglucosamine is NAG, salicin is SAL, arbutin is ARB, D-tagatose is TAG, potassium gluconate is GNT, 5-ketogluconate is 5KG, inositol is INO, methyl-αD-mannopyranoside is MDM, methyl-βD-xylopyranoside is MDX, xylitol is XLT, N-acetyl-d-galactosamine is NDG, lysose is LYX, α-cyclodextrin is DEX, and aesculin is ESC.

[0020] 2. Determine the calculation method in the numerical method The calculation method for pattern frequency, total occurrence frequency, multiple occurrence frequency, identification percentage, and pattern likelihood ratio T-value for each taxonomic unit is determined according to the bacterial probability identification theory (based on the lactic acid bacteria biochemical reaction positive probability database in Table 1; if the probability value is 100%, use 99% instead; if the probability value is 0%, use 1%). The specific calculation function is as follows: mode frequency It is the product of the maximum total probability of occurrence of biochemical experimental combinations under this classification unit. In the formula: LJ represents the probability of taxonomic unit J occurring in all biochemical tests. X represents the probability of a certain biochemical test occurring for classification unit J. i For the i-th biochemical experiment; Total frequency of occurrence This is the product of the actual occurrence probabilities of a given unknown bacterium in the biochemical test combinations within this taxonomic unit. ; The total occurrence frequency S is the sum of the total occurrence frequencies of each classification unit. ; The identification percentage ID is calculated by dividing the total frequency of each classification unit by the total frequency of multiple items, then multiplying by 100%. The sum of all identification percentages should equal 100%. ; The pattern likelihood ratio T is calculated by dividing the total frequency of each class unit by the pattern frequency of that class unit, and then multiplying by 100%. .

[0021] 3. Determine the evaluation criteria for results. The reliability of the identification results was determined based on the ratio of identification percentage (ID) to pattern likelihood (T) according to the evaluation criteria in Table 3. The results display provides both Chinese and Latin translations for each taxonomic unit.

[0022] Table 3 Evaluation of the identification results ≥90% ≥90% Excellent identification results ≥90% [50%，90%） Excellent identification results ≥90% [20%，50%） Good identification results ≥90% <20% Acceptable identification results [80%，90%） ≥60% Excellent identification results [80%，90%） [30%，60%） Good identification results [80%，90%） [1%，30%） Acceptable identification results [80%，90%） [0.01%，1%） Suspicious identification results [60%，80%） ≥50% Good identification results [60%，80%） [10%，50%） Acceptable identification results [60%，80%） [0.01%，10%） Suspicious identification results [60%，90%） <0.01% Unacceptable identification results <60% ≥10% Suspicious identification results <60% <10% Unacceptable identification results Example 4: Establishing a process for identifying test strains

[0023] In a biosafety cabinet, the test strain was inoculated onto an MRS medium plate, inverted, and incubated in an anaerobic incubator (36±1℃) for 48±1 h. The above 47 biochemical experiments were performed, and the prepared identification strips were transferred to the anaerobic incubator (36±1℃) for 48±1 h. After obtaining the biochemical test results of the test strain, the results were first converted into probability values ​​based on the recorded negative or positive biochemical reactions. Then, the pattern frequency, total occurrence frequency, multiple total occurrence frequency, identification percentage, and pattern likelihood ratio (T-value) of each taxonomic unit of the test strain were calculated according to the calculation method in Example 3.

[0024] Due to space limitations, it is not possible to list all 21 positive rates for each taxonomic unit. Therefore, examples will be provided for taxonomic units A, B, and C, as well as their biochemical experiments 1, 2, 3, 4, and 5.

[0025] The biochemical test results and the positive rates of taxonomic units A, B, and C of the tested strains are shown in Table 4. Table 4. Biochemical test results of the test bacteria and the positive rate of reactions with known bacteria Classification Unit A 99 95 98 Classification Unit B 99 11 93 Classification Unit C 0 23 100 Test strain + - + Mode frequency: Pa (Classification Unit A) = 0.99 × 0.95 × 0.98 = 0.92169; Pa(Classification Unit B) = 0.99 × 0.89 × 0.93 = 0.819423; Pa (classification unit C) = 0.99 × 0.77 × 0.99 = 0.754677; Total probability of occurrence: Pb(Classification Unit A) = 0.99 × 0.05 × 0.98 = 0.04851; Pb(Classification Unit B) = 0.99 × 0.89 × 0.93 = 0.819423; Pb(taxonomic unit C) = 0.01 × 0.77 × 0.99 = 0.007623; Total probability of occurrence of multiple events: S=0.04851+0.819423+0.007623=0.875556; ID value: ID (Classification Unit A) = 0.04851 / 0.875556 × 100% = 5.54%; ID (Classification Unit B) = 0.819423 / 0.875556 × 100% = 93.59%; ID (Classification Unit C) = 0.007623 / 0.875556 × 100% = 0.87%; T-value: T(Classification Unit A) = 0.04851 / 0.92169 × 100% = 5.26%; T(Classification Unit B) = 0.819423 / 0.819423 = 100%; T(Classification Unit C) = 0.007623 / 0.754677 = 1.01%; The samples were sorted in descending order of their identification percentages. The results were evaluated according to the criteria described in Example 3. In this example, taxonomic unit B, with a maximum score of 93.59%, was selected. The final identification result was: the tested bacteria were known species corresponding to taxonomic unit B, with an identification percentage of 93.59% and a model likelihood ratio (T) of 100.00%, indicating an excellent identification result. Inconsistencies between the tested strain and each taxonomic unit in the identification results were listed separately. The results also included Chinese and Latin translations for each taxonomic unit. Example 5: Evaluation of the effectiveness of a rapid identification method for lactic acid bacteria counts

[0026] To verify the feasibility of the rapid numerical identification method for lactic acid bacteria, 14 standard strains and 171 isolates of lactic acid bacteria were selected as test subjects. The strains were identified according to the procedure described in Example 5. After obtaining the biochemical experimental results, the identification percentage and model likelihood ratio (T-value) of each taxonomic unit were calculated based on the numerical identification method established in Example 3. The identification percentages were arranged in descending order, and the strain corresponding to the highest identification percentage was the taxonomic unit to which the standard strain belonged. The reliability of the identification results was determined according to the evaluation criteria in Table 3. The results of the numerical identification method for the standard strains are shown in Table 5.

[0027] Table 5 Evaluation of the results of the numerical identification system for standard strains of lactic acid bacteria CMCC(B)34130 99.83 1.37 Acceptable identification results FSCC(I)173059 94.01 0.48 Acceptable identification results FSCC(I)173056 98.49 13.82 Acceptable identification results GIM1.14 96.99 9.84 Acceptable identification results ATCC14917 99.99 2.15 Acceptable identification results ATCC8014 96.10 0.93 Acceptable identification results FSCC(I)173060 97.21 0.00 Acceptable identification results CMCC(B)34135 100.00 56.10 Excellent identification results CMCC(B)34133 99.94 59.21 Excellent identification results CMCC(B)32483 99.54 0.00 Acceptable identification results CICC6032 60.38 14.93 Acceptable identification results CMCC(B)34131subsp subsp 81.74 2.13 Acceptable identification results AS1.1878 97.82 0.02 Acceptable identification results ATCC7469 99.83 65.77 Excellent identification results The 185 lactic acid bacteria strains yielded 138 biochemical profiles. Analysis of the reliability of the identification results showed that they had acceptable identification efficacy for most of the tested lactic acid bacteria.

[0028] Using the identification results of MALDI-TOF MS and 16S rRNA gene sequencing results as reference standards, the results were compared with those of the numerical identification method for lactic acid bacteria strains of this invention. The results showed that the overall identification accuracy of the numerical identification method for 185 lactic acid bacteria strains in this study reached 98.92% (Table 6). Overall, the numerical identification method for lactic acid bacteria strains established in this invention has a high level of identification capability.

[0029] Table 6. Accuracy of the numerical identification system for lactic acid bacteria of the present invention 14 14 14 100.00 100.00 9 9 9 100.00 100.00 23 22 23 95.65 100.00 7 7 7 100.00 100.00 17 17 17 100.00 100.00 9 9 9 100.00 100.00 3 3 3 100.00 100.00 15 15 14 100.00 93.33 2 2 2 100.00 100.00 12 12 12 100.00 100.00 5 0 4 0 100.00 13 13 13 100.00 100.00 5 5 5 100.00 100.00 4 4 4 100.00 100.00 10 10 10 100.00 100.00 5 5 5 100.00 100.00 4 4 4 100.00 100.00 4 4 4 100.00 100.00 4 4 4 100.00 100.00 1 1 1 100.00 100.00 6 6 6 100.00 100.00 2 2 2 100.00 100.00 3 3 3 100.00 100.00 8 8 8 100.00 100.00 Total 185 179 183 96.76 98.92 Note: The accuracy rate is the accuracy rate of the numerical identification method for lactic acid bacteria strains of the present invention. The calculation formula is: Accuracy rate (%) = Number of strains consistent with the numerical identification method and MALDI-TOF MS and 16S rRNA gene sequencing results / Number of strains with MALDI-TOF MS and 16S rRNA gene sequencing results × 100%.

[0030] The above is a further detailed description of the present invention and should not be considered as a limitation on the specific implementation of the present invention. For those skilled in the art, simple deductions or substitutions without departing from the concept of the present invention are all within the protection scope of the present invention.

Claims

1. A numerical identification method for lactic acid bacteria strains, comprising the following steps: Biochemical reaction experiments were conducted on different known lactic acid bacteria species. The biochemical reaction experiments included the following 47 types of biochemical reactions: D-lactose, D-cellobiose, D-merinobiose, D-minotriose, D-raffinose, L-rhamnose, D-minobiose, eugenol, D-calendol, D-glucose, D-arabinose, D-fructose, amygdalin, glycogen, erythritol, L-xylose, D-arabinol, L-arabinol, glycine, D-trehalose, D-xylose, mannitol, and yamylose. Biochemical experiments on pear alcohol, starch, D-sucrose, D-galactose, D-maltose, D-mannose, inulin, L-arabinose, D-ribose, L-sorbose, methyl-α-D-glucopyranoside, N-acetylglucosamine, salicin, arbutin, D-tagatose, potassium gluconate, potassium 5-ketogluconate, inositol, methyl-α-D-mannopyranoside, methyl-β-D-xylopyranoside, xylitol, N-acetyl-d-galactosamine, lythose, α-cyclodextrin, and aesculin; The biochemical reaction positivity rates of different types of lactic acid bacteria were obtained, and different known types of lactic acid bacteria were used as different taxonomic units to determine the probability matrix of each taxonomic unit. Calculate the pattern frequency of the classification unit Total frequency of occurrence The total occurrence frequency S, the identification percentage ID, and the pattern likelihood ratio T are multiple factors, where: mode frequency It is the product of the maximum total probability of occurrence of biochemical experimental combinations under this classification unit. In the formula: LJ represents the probability of taxonomic unit J occurring in all biochemical tests. X represents the probability of a certain biochemical test occurring for classification unit J. i For the i-th biochemical experiment; Total frequency of occurrence This is the product of the actual occurrence probabilities of a given unknown bacterium in the biochemical test combinations within this taxonomic unit. ; The total occurrence frequency S is the sum of the total occurrence frequencies of each classification unit. ; The identification percentage ID is calculated by dividing the total frequency of each classification unit by the total frequency of multiple items, then multiplying by 100%. The sum of all identification percentages should equal 100%. ; The pattern likelihood ratio T is calculated by dividing the total frequency of each class unit by the pattern frequency of that class unit, and then multiplying by 100%. ; After the bacterial strains to be identified were cultured in a selective culture medium for lactic acid bacteria, biochemical reaction experiments were performed. Based on the positive probability data of the lactic acid bacteria biochemical reactions, the negative / positive test results were converted into probability values, and the pattern frequency of the bacterial strains to be identified was calculated. Total frequency of occurrence Multiple total occurrence frequency S, identification percentage ID, and pattern likelihood ratio T; Based on the identification percentage (ID) and pattern likelihood ratio (T) of the strain to be identified, the reliability of the identification results of the strain to be identified is evaluated, and the taxonomic unit with the highest score is the identification result of the strain to be identified.

2. The numerical identification method according to claim 1, characterized in that, The positive probability data of the biochemical reaction of lactic acid bacteria is obtained by statistical analysis of the biochemical reaction experiments using known standard strains and isolates of the genus Lactobacillus, or by further combining publicly available data.

3. The numerical identification method according to claim 1, characterized in that, When converting negative / positive test results into probability values, the probability value of a positive test result is the known positive probability value, and the probability value of a negative test result is (100% - the known positive probability value).

4. The numerical identification method according to claim 2, characterized in that, To avoid extreme values, if the probability value is 0% or 100%, a value close to 0 or close to 100% should be used instead.

5. The numerical identification method according to claim 4, characterized in that, A value greater than 0 is close to 1%, and a value less than 100% is close to 99%.

6. The numerical identification method according to any one of claims 1 to 5, characterized in that, The lactic acid bacteria strains include the following 44 strains: Pediococcus lactis ( Pediococcus acidilactici ), Pediococcus pentosaceus ( Pediococcus pentosaceus Lactobacillus plantarum ( Lactiplantibacillus plantarum ), fermenting lactobacillus ( Limosilactobacillus fermentum ), Lactobacillus brevis ( Levilactobacillus brevis ), Leuconostoc membranaceus ( Leuconostoc mesenteroides ), Leuconostoc membranaceus subsp. glucanoides ( Leuconostoc mesenteroides subsp. dextranicum ), Leuconostoc membranaceus subsp. milk fat ( Leuconostoc mesenteroides subsp. cremoris Leuconostoc mesenteroides ( Leuconostoc lactis ), Lactobacillus casei ( Lacticaseibacillus paracasei ), Lactobacillus casei subsp. tough ( Lacticaseibacillus paracasei subsp. tolerans ), Lactococcus lactis ( Lactococcus lactis ), Lactobacillus pentosolicus ( Lactiplantibacillus pentosus ), Clostridium perfringens ( Carnobacterium divergens ), Weissella greenis ( Weissella viridescens ), confused Weissella ( Weissella confusa ), Streptococcus thermophilus ( Streptococcus thermophilus Leuconostoc mesenteroides ( ), Leuconostoc mesenteroides Leuconostoc citreum ), Lactobacillus brucellosis ( Lentilactobacillus buchneri Lactobacillus helveticus ( Lactobacillus helveticus Lactobacillus delbrueckii subsp. bulgaricus ( Lactobacillus delbrueckii subsp. bulgaricus Lactobacillus delbrueckii subsp. delbrueckii ( Lactobacillus delbrueckii subsp. delbrueckii Lactobacillus delbrueckii subsp. lactis ( Lactobacillus delbrueckii subsp. lactis ), Curved Lactobacillus ( Latilactobacillus curvatus ), Lactobacillus acidophilus ( Lactobacillus acidophilus Lactobacillus rhamnosus ( Lacticaseibacillus rhamnosus ), Green balloon bacteria ( Aerococcus viridans ), Heat-killing Sossylvatica ( Brochothrix thermophacta ), Maltodactylum indicum ( Carnobacterium maltaromaticum ), hill-shaped secondary fermentation lactobacillus ( Secundilactobacillus collinoides ), Lactobacillus fructose-loving fruit ( Fructilactobacillus fructivorans ), Lactobacillus lindneri ( Fructilactobacillus lindneri ), salivary host-associated lactobacillus ( Ligilactobacillus salivarius ), Lactococcus raffinis ( Pseudolactococcus raffinolactis ), harmful Pediococcus ( Pediococcus damnosus ), Halophilic Tetracoccus ( Tetragenococcus halophilus Lactobacillus curvature () Lactobacillus crispatus ), Leuconostoc membranaceus subsp. enterica ( Leuconostoc mesenteroides subsp. mesenteroides ), Lactobacillus brucellosis ( Lentilactobacillus parabuchneri Sake is widely covered with Lactobacillus ( Latilactobacillus sakei Lactobacillus subsp. fleshy in sake ( ) Latilactobacillus sakei subsp. carnosus Lactobacillus sacchariformis (Lactobacillus sacchariformis subsp. sake Latilactobacillus sakei subsp. sakei Lactobacillus casei ( Lacticaseibacillus casei ), Lactococcus fatii ( Lactococcus cremoris ).

7. The numerical identification method according to claim 6, characterized in that, The positive probability (%) of the different biochemical reaction experiments of the lactic acid bacteria is shown in the table below: Continued from the table above Continued from the table above Continued from the table above The strains numbered 1-44 in the table are as follows:

1. *Pediococcus lactis*, 2. *Pediococcus pentosaccharis*, 3. *Lactobacillus plantarum*, 4. *Lactobacillus fermentum*, 5. *Lactobacillus brevis*, 6. *Leuconostoc membranaceus*, 7. *Leuconostoc membranaceus* subsp. *glucan*, 8. *Leuconostoc membranaceus* subsp. *milk fat*, 9. *Leuconostoc membranaceus* subsp. *membranaceus*, 10. *Leuconostoc membranaceus*, 11. *Lactobacillus casei*, 12. *Lactobacillus casei* subsp. *tough*, 13. *Lactococcus lactis*, 14. *Lactococcus fat*, 15. *Lactobacillus pentosaccharis*, 16. *Clostridium perfringens*, 17. *Weissella viride*, 18. *Weissella confounding*, 19. *Streptococcus thermophilus*, 20. *Leuconostoc citrinum*, 21. *Lactobacillus brucelli*, 22. *Lactobacillus brucelli*, 23. *Lactobacillus helveticus*, 24.

25. *Lactobacillus delbrueckii* subsp. *bulgaricus*, 26. *Lactobacillus delbrueckii* subsp. *delbrueckii*, 27. *Lactobacillus curvilinearis*, 28. *Lactobacillus acidophilus*, 29. *Lactobacillus rhamnosus*, 30. *Lactobacillus sakei*, 31. *Lactobacillus sakei* subsp. *flesh*, 32. *Lactobacillus sakei* subsp. *sake*, 33. *Lactobacillus casei*, 34. *Bacillus viridans*, 35. *Synthiasis heat-killing*, 36. *Mallotus maltophilia*, 37. *Lactobacillus hillockii*, 38. *Lactobacillus fructosei*, 39. *Lactobacillus lindneri*, 40. *Lactobacillus salivati-associated*, 41. *Lactococcus raffinis*, 42. *Pediococcus maltophilia*, 43. *Tetraphytic halophila*, 44. *Lactobacillus curvilinearis*; The data in the table represent the percentage positive rate of each biochemical reaction. The names and abbreviations of the biochemical reactions are as follows: D-lactose is LAC, D-cellobiose is CEL, D-merinobiose is MEL, D-minotriose is MLZ, D-raffinose is RAF, L-rhamnose is RHA, D-minobiose is TUR, eugenol is DUL, D-calendol is ADO, D-glucose is GLU, D-arabinose is DARA, D-fructose is FRU, amygdalin is AMY, glycogen is GLYG, erythritol is ERY, L-xylose is LXYL, D-arabinol is DARL, L-arabinol is LARL, glycerol is GLY, D-trehalose is TRE, D-xylose is DXYL, mannitol is MAN, sorbitol is SO B. Starch is AMD, D-sucrose is SAC, D-galactose is GAL, D-maltose is MAL, D-mannose is MNE, inulin is INU, L-arabinose is LARA, D-ribose is RIB, L-sorbose is SBE, methyl-αD-glucopyranoside is MDG, N-acetylglucosamine is NAG, salicin is SAL, arbutin is ARB, D-tagatose is TAG, potassium gluconate is GNT, 5-ketogluconate is 5KG, inositol is INO, methyl-αD-mannopyranoside is MDM, methyl-βD-xylopyranoside is MDX, xylitol is XLT, N-acetyl-d-galactosamine is NDG, lysose is LYX, α-cyclodextrin is DEX, and aesculin is ESC.

8. The numerical identification method according to any one of claims 1 to 5, characterized in that, The criteria for determining the credibility of the evaluation results are as follows: 。 9. The numerical identification method according to any one of claims 1 to 5, characterized in that, The bacterial strains to be tested came from food samples and environmental samples.

10. The numerical identification method according to any one of claims 1 to 5, characterized in that, Biochemical experiments showing inconsistencies between the test strain and each taxonomic unit were listed separately, and the test strain was identified and determined using other known methods.

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