You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 beer_factory
【Schema】
# Table: rootbeer
[
(RootBeerID:INTEGER, Primary Key, Examples: [100000, 100001, 100002]),
(BrandID:INTEGER, Examples: [10001, 10002, 10003]),
(ContainerType:TEXT, Examples: [Bottle, Can]),
(LocationID:INTEGER, Examples: [1, 2]),
(PurchaseDate:DATE, Examples: [2015-07-03])
]
# Table: location
[
(LocationID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(LocationName:TEXT, Examples: [LOST]),
(StreetAddress:TEXT, Examples: [6000 J St]),
(City:TEXT, Examples: [Sacramento]),
(State:TEXT, Examples: [CA]),
(ZipCode:INTEGER, Examples: [95819])
]
# Table: transaction
[
(TransactionID:INTEGER, Primary Key, Examples: [100000, 100001, 100002]),
(CreditCardNumber:INTEGER, Examples: [6011583832864739, 4716634257568793, 5597608517846968]),
(CustomerID:INTEGER, Examples: [864896, 610766, 207846]),
(TransactionDate:DATE, Examples: [2014-07-07]),
(CreditCardType:TEXT, Examples: [Discover, Visa, MasterCard]),
(LocationID:INTEGER, Examples: [2, 1]),
(RootBeerID:INTEGER, Examples: [105661, 105798, 102514]),
(PurchasePrice:REAL, Examples: [3.0, 1.0])
]
# Table: geolocation
[
(LocationID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(Latitude:REAL, Examples: [0.0, 38.566129, 38.559615]),
(Longitude:REAL, Examples: [0.0, -121.426432, -121.42243])
]
# Table: customers
[
(CustomerID:INTEGER, Primary Key, Examples: [101811, 103508, 104939]),
(First:TEXT, Examples: [Kenneth, Madeleine, Damen]),
(Last:TEXT, Examples: [Walton, Jones, Wheeler]),
(StreetAddress:TEXT, Examples: [6715 Commonwealth Dr, 3603 Leola Way, 6740 Branwood Way]),
(City:TEXT, Examples: [Sacramento, Orangevale, Folsom]),
(State:TEXT, Examples: [CA]),
(ZipCode:INTEGER, Examples: [94256, 94258, 95662]),
(Email:TEXT),
(PhoneNumber:TEXT, Examples: [(916) 918-1561, (916) 186-9423, (916) 164-1156]),
(FirstPurchaseDate:DATE, Examples: [2013-05-30]),
(SubscribedToEmailList:TEXT, Examples: [FALSE, TRUE]),
(Gender:TEXT, Examples: [M, F])
]
# Table: rootbeerreview
[
(CustomerID:INTEGER, Primary Key, Examples: [101811, 105549, 105771]),
(BrandID:INTEGER, Primary Key, Examples: [10012, 10014, 10015]),
(StarRating:INTEGER, Examples: [5, 1, 3]),
(ReviewDate:DATE, Examples: [2013-07-15]),
(Review:TEXT)
]
# Table: rootbeerbrand
[
(BrandID:INTEGER, Primary Key, Examples: [10001, 10002, 10003]),
(BrandName:TEXT, Examples: [A&W, A.J. Stephans, Abita]),
(FirstBrewedYear:INTEGER, Examples: [1919, 1926, 1986]),
(BreweryName:TEXT, Examples: [Dr Pepper Snapple Group]),
(City:TEXT, Examples: [Lodi, Fall River, Covington]),
(State:TEXT, Examples: [CA, MA, LA]),
(Country:TEXT, Examples: [United States, Australia]),
(Description:TEXT),
(CaneSugar:TEXT, Examples: [FALSE, TRUE]),
(CornSyrup:TEXT, Examples: [TRUE, FALSE]),
(Honey:TEXT, Examples: [FALSE, TRUE]),
(ArtificialSweetener:TEXT, Examples: [FALSE, TRUE]),
(Caffeinated:TEXT, Examples: [FALSE, TRUE]),
(Alcoholic:TEXT, Examples: [FALSE]),
(AvailableInCans:TEXT, Examples: [TRUE, FALSE]),
(AvailableInBottles:TEXT, Examples: [FALSE, TRUE]),
(AvailableInKegs:TEXT, Examples: [FALSE, TRUE]),
(Website:TEXT),
(FacebookPage:TEXT),
(Twitter:TEXT),
(WholesaleCost:REAL, Examples: [0.42, 0.98, 1.13]),
(CurrentRetailPrice:REAL, Examples: [1.0, 3.0, 44.0])
]
【Foreign keys】
rootbeer.LocationID=geolocation.LocationID
rootbeer.LocationID=location.LocationID
rootbeer.BrandID=rootbeerbrand.BrandID
location.LocationID=geolocation.LocationID
transaction.CustomerID=customers.CustomerID
transaction.LocationID=location.LocationID
transaction.RootBeerID=rootbeer.RootBeerID
geolocation.LocationID=location.LocationID
rootbeerreview.CustomerID=customers.CustomerID
rootbeerreview.BrandID=rootbeerbrand.BrandID

    【Question】
    List out root beer brand that is not caffeinated and not containing cane sugar. What is the total amount sold for this products? root beer brand refers to BrandName; not caffeinated refers to Caffeinated = 'FALSE'; not containing cane sugar refers to CaneSugar = 'FALSE'; total amount sold = SUM(PurchasePrice);

    【Evidence】
    root beer brand refers to BrandName; not caffeinated refers to Caffeinated = 'FALSE'; not containing cane sugar refers to CaneSugar = 'FALSE'; total amount sold = SUM(PurchasePrice);

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T1.BrandName, SUM(T3.PurchasePrice) FROM rootbeerbrand AS T1 INNER JOIN rootbeer AS T2 ON T1.BrandID = T2.BrandID INNER JOIN `transaction` AS T3 ON T2.RootBeerID = T3.RootBeerID WHERE T1.CaneSugar = 'FALSE' AND T1.Caffeinated = 'FALSE' GROUP BY T1.BrandName